Integrated processing method and system for machine learning model deployment

Abstract

The application provides a machine learning model deployment integrated processing method and system. The method comprises the following steps: obtaining a model file uploaded by a user; identifying a framework type and calling a parser to determine a model analysis report; determining a parameter recommendation scheme based on the model analysis report and a deployment target environment selected by the user, and performing effectiveness verification to obtain a parameter configuration scheme; generating an environment preparation script and a standardized model file based on the parameter configuration scheme and dependency information, thereby generating an interface file, forming an environment deployment configuration file based on the parameter configuration scheme and the deployment target environment, and constituting a deployment data package; obtaining target environment credentials; executing automatic deployment based on the target environment credentials and the deployment data package, and performing running quality evaluation according to the deployment result and the parameter configuration scheme to obtain a service quality evaluation result, and optimizing the parameter configuration scheme and the deployment data package. The application simplifies the model deployment process, improves the deployment efficiency and running stability.

Description

Technical Field

[0001] This invention relates to the fields of artificial intelligence and computer technology, and specifically to an integrated processing method and system for deploying machine learning models. Background Technology

[0002] Deploying machine learning models is a crucial step in the practical application of artificial intelligence, involving the entire process of migrating trained models from the development environment to the production environment and providing stable services. This technical field focuses on how to efficiently and reliably put machine learning models into practical applications, including multiple technical aspects such as model file parsing, parameter configuration, environment adaptation, and operational monitoring.

[0003] In current technical practices, common model deployment methods include container-based deployment solutions and cloud service-based managed deployments. Container-based deployment solutions typically use Docker to encapsulate the model and its dependencies, combined with Kubernetes to achieve elastic scaling; while cloud service-based managed deployments utilize the one-click deployment capabilities provided by platforms such as AWS SageMaker and Azure ML to simplify the deployment process. These methods have certain practical applications in specific scenarios, but the overall solutions remain relatively fragmented.

[0004] In existing technologies, model parsing, parameter configuration, and deployment are typically handled as separate steps. Model files are exported from the training environment manually or via scripts, parameters are manually configured, and then deployed to the inference server. This approach relies on framework-specific APIs for model export and loading, such as PyTorch's `torch.save / load` or XGBoost's `Booster.save_model`, and inference parameters are set via hard-coding or configuration files. The deployment phase requires writing deployment scripts specific to the environment, building container images, and configuring resource allocation.

[0005] However, this decentralized approach has significant technical drawbacks: First, the connections between different stages are loose, requiring substantial manual intervention, resulting in a lengthy and error-prone deployment process. Second, there is poor compatibility between different model frameworks and deployment environments, necessitating the development of customized logic for each combination, leading to high adaptation costs. Third, parameter configuration relies heavily on human experience, making dynamic optimization based on model characteristics and the operating environment impossible, hindering efficient resource utilization. These issues severely restrict the rapid deployment and large-scale application of machine learning models. Summary of the Invention

[0006] In view of this, embodiments of this application provide an integrated processing method and system for machine learning model deployment, which can automatically identify model framework types and parse model structures, intelligently recommend deployment parameters and verify their effectiveness, automatically generate standardized deployment files, perform automated deployment and provide end-to-end monitoring and optimization feedback, thereby greatly simplifying the model deployment process and improving deployment efficiency and operational stability.

[0007] In a first aspect, embodiments of this application provide an integrated processing method for deploying machine learning models, including: Retrieve the model file uploaded by the user; The framework type is identified and the corresponding parser is invoked based on the file characteristics of the model file. The framework model and the parser are then parsed to determine the model parsing report, which includes the model's structured data and dependency information. Based on the model analysis report and the deployment target environment selected by the user, a parameter recommendation scheme is determined, and the validity of the parameter recommendation scheme is verified to obtain a parameter configuration scheme. Based on the parameter configuration scheme and the dependency information, an environment preparation script and a standardized model file after standardization are generated. Based on the model structured data and the parameter configuration scheme, an interface file is generated. Based on the parameter configuration scheme and the deployment target environment, an environment deployment configuration file is formed. Based on the environment preparation script, the standardized model file, the interface file, and the environment deployment configuration file, a deployment data package is constructed; Obtain the user's target environment credentials, which are used to indicate the environment connection status; Based on the target environment credentials and the deployment data package, automated deployment is performed to obtain deployment results. Based on the deployment results and the parameter configuration scheme, an operational quality assessment is conducted to obtain a service quality assessment result. Based on the service quality assessment result, the parameter configuration scheme and the deployment data package are optimized.

[0008] As an improvement to the above solution, the step of identifying the framework type and calling the corresponding parser based on the file characteristics of the model file, and parsing the framework model and the parser to determine the model parsing report includes: The model file is subjected to security verification to obtain verification results, which include file type verification results, size limit check results, and virus scan results. After the verification result is passed, the model file is saved to a temporary storage area to generate a corresponding identifier, and the model file path and model meta-information are obtained. Based on the model file path and the model meta information, the initial judgment frame type is identified by the file extension, and the initial judgment frame type is identified by a preset scene loading model. The corresponding parser is selected to obtain the frame type and the parser instance corresponding to the parser. The scene loading model is used to indicate the loading of secondary confirmation in a preset fuzzy scene. Based on the framework type and the parser instance, model structured data is generated, which includes network structure, computational characteristic information, and feature information. Based on the model file and the framework type, the model meta information is scanned to extract framework version dependencies, hardware dependencies, and environment configuration requirements, and corresponding dependency information is generated. Based on the structured data of the model and the dependency information, a model parsing report is generated. The step of generating structured model data based on the framework type and the parser instance includes: Based on the framework type and the parser instance, a preset framework API is called to extract network structure information, which includes network layer type, input and output tensor shape, and number of parameters. Based on the network structure information, the computational characteristics of the model are determined, including the number of floating-point operations and computational complexity. Based on the network structure information, the feature information of the model is extracted, including the input feature dimension, the output feature dimension, and the feature type. Based on the network structure information, the computational characteristic information, and the feature information, the model structured data is generated.

[0009] As an improvement to the above solution, the step of determining a parameter recommendation scheme based on the model analysis report and the user-selected deployment target environment, and validating the parameter recommendation scheme to obtain a parameter configuration scheme, includes: Based on the model analysis report, analyze the configurable parameter items; Based on the preset model type and preset framework characteristics, a configuration interface is generated, and the default values ​​and optional ranges are displayed on the configuration interface. Based on the configurable parameter items, the configuration interface, the default values ​​and the optional ranges, an interactive parameter configuration interface is generated and displayed. The system obtains the deployment target environment selected by the user on the interactive parameter configuration interface, and obtains the environment resource characteristics, including GPU type and memory capacity. Based on the target deployment environment and the characteristics of the environmental resources, the environmental constraints are queried to obtain the corresponding deployment environment information; Based on the model analysis report and the deployment environment information, the matching degree between the model's computational characteristics and the environment's resource characteristics is analyzed using a preset parameter optimization model, and configuration parameters are predicted. Based on the model's computational characteristics, the matching degree, and the predicted configuration parameters, a parameter recommendation scheme is obtained, wherein the configuration parameters include resource configuration parameters and runtime configuration parameters. Based on the parameter recommendation scheme, the configuration parameters are verified to meet resource constraints and verification performance requirements, corresponding verification results are generated, and adjustment suggestions are generated based on the verification results to obtain target parameter configuration data. Based on the target parameter configuration data, static deployment parameters and dynamic operation parameters are integrated to generate a parameter configuration scheme.

[0010] As an improvement to the above solution, the step of generating an environment preparation script and a standardized model file after standardization transformation based on the parameter configuration scheme and the dependency information, generating an interface file based on the model structured data and the parameter configuration scheme, and forming an environment deployment configuration file based on the parameter configuration scheme and the deployment target environment, includes: Based on the dependency information and parameter configuration scheme in the model parsing report, the environment preparation script is generated by initializing the environment script and then encapsulating it. Based on the model parsing report and the parameter configuration scheme, an intermediate representation format suitable for the target environment is selected, and the model file is converted into a standardized model file according to the intermediate representation format using a preset call conversion model. Based on the input and output feature information in the model analysis report and the parameter configuration scheme, define the service API interface and generate the corresponding interface file based on the service API interface; Based on the parameter configuration scheme and the target deployment environment, select the corresponding environment deployment template, and form an environment deployment configuration file according to the environment deployment template.

[0011] As an improvement to the above solution, the deployment data package is constructed based on the environment preparation script, the standardized model file, the interface file, and the environment deployment configuration file, including: Based on the environment preparation script, the standardized model file, the interface file, and the environment deployment configuration file, create a deployment package directory structure; Based on the deployment package directory structure, the environment preparation script is copied to the script directory, the standardized model file is copied to the model directory, the interface file is copied to the interface directory, and the environment deployment configuration file is copied to the configuration directory to generate the deployment package directory; Based on the deployment package directory, generate container build files; Based on the deployment package directory and the container build file, a compression and packaging operation is performed to obtain the deployment data package.

[0012] As an improvement to the above solution, the step of performing automated deployment based on the target environment credentials and the deployment data package to obtain deployment results, and performing operational quality assessment based on the deployment results and the parameter configuration scheme to obtain service quality assessment results, and optimizing the parameter configuration scheme and the deployment data package based on the service quality assessment results, includes: Based on the environment connection status and the deployment data packet, the deployment file is unpacked, and the corresponding environment API is called to perform the deployment operation to obtain the deployment result; Based on the deployment results and the parameter configuration scheme, the monitoring system data is configured, and the collection indicators and collection frequency are set. Based on the monitoring system data, the collection indicators and the collection frequency, the monitoring configuration data and initial state data are obtained. Based on the monitoring configuration data and the initial state data, model service operation data is collected, and periodic quality assessments are performed to obtain service quality assessment results. Based on the service quality assessment results and the model service operation data, performance anomalies and quality deviations are detected, corresponding alarm information and optimization suggestions are generated, and the parameter configuration scheme and the deployment data package are optimized according to the optimization suggestions and the optimized service status.

[0013] As an improvement to the above solution, the step of extracting network structure information by calling a preset framework API based on the framework type and the parser instance includes: Based on the network structure information, the original feature information is extracted, which includes feature name, data type and dimension; Based on the original feature information, a corresponding mutual information calculation engine is selected based on the variable type. The variable type includes continuous variables, discrete-continuous variable pairs, and discrete variables. The mutual information calculation engine includes a preset k-nearest neighbor-based mutual information estimator selected for the continuous variable pairs, a preset histogram-based mutual information estimator selected for the discrete-continuous variable pairs, and a preset frequency-based mutual information estimator selected for the discrete variable pairs. Based on the mutual information calculation engine, the mutual information value between each original feature information and the target variable is calculated, and the feature importance ranking is generated based on the mutual information value to obtain the corresponding enhanced feature importance report; The network structure information is updated based on the enhanced feature importance report.

[0014] As an improvement to the above scheme, updating the network structure information based on the enhanced feature importance report includes: Based on the enhanced feature importance report, a mutual information matrix between features is calculated, and based on the mutual information matrix, highly correlated feature groups are identified, so that a feature correlation matrix is ​​obtained from the highly correlated feature groups; Based on the feature correlation matrix, a feature dependency graph is constructed using a preset graph theory model, wherein each node in the feature dependency graph represents the original feature information, and the edge weights in the feature dependency graph represent the mutual information values. Community detection is performed on the feature dependency graph to identify feature subsets whose correlation is greater than a preset correlation threshold, and the centrality index of each node is calculated based on the feature subsets to obtain feature structure information. The network structure information is updated based on the structured feature information and the enhanced feature importance report.

[0015] As an improvement to the above solution, based on the model analysis report and the deployment environment information, a preset parameter optimization model is used to analyze the matching degree between the model's computational characteristics and the environmental resource characteristics, and configuration parameters are predicted. Based on the model's computational characteristics, the matching degree, and the predicted configuration parameters, a parameter recommendation scheme is obtained, including: Based on the historical deployment data obtained in advance, a parameter performance mapping matrix is ​​constructed, wherein each row of the parameter performance mapping matrix represents a set of historical deployment data, and each column of the parameter performance mapping matrix represents a performance index. Based on the parameter performance mapping matrix, a corresponding penalty form is selected according to the type parameter to determine the hybrid non-convex penalty function. The type parameter includes discrete resource allocation parameters, continuous optimization parameters, and parameters that need to be grouped. The parameters that need to be grouped include memory parameters and concurrency parameters. The penalty form includes truncated L1 penalty corresponding to the discrete resource allocation parameters, smooth shearing absolute deviation penalty corresponding to the continuous optimization parameters, and grouping MCP penalty corresponding to the parameters that need to be grouped. Based on the hybrid nonconvex penalty function, an objective function and a corresponding optimization objective are constructed. The optimization objective is used to indicate minimizing the weighted sum of the performance prediction error of the objective function and the nonconvex penalty term. Based on the optimization objective, the objective function is solved using a preset coordinate descent model or a preset proximal gradient model to obtain a sparse parameter weight matrix. Based on the model analysis report, the model computation characteristics are extracted, and based on the model computation characteristics, the model computation requirements information is obtained. The model computation characteristics include the number of floating-point operations, the number of parameters, and the memory requirements. Based on the deployment environment information, environmental resource characteristics are extracted, and environmental resource information is obtained based on the environmental resource characteristics. Based on the model calculation demand information and the environmental resource information, the parameter optimization model is used to generate the matching degree between the model calculation characteristics and the environmental resource characteristics, and a matching degree score corresponding to the matching degree is obtained. Based on the matching score and the sparse parameter weight matrix, the parameter optimization model is used to predict static deployment parameters and dynamic operation parameters to obtain the parameter recommendation scheme.

[0016] As an improvement to the above scheme, the step of predicting static deployment parameters and dynamic operating parameters using the parameter optimization model based on the matching degree score and the sparse parameter weight matrix to obtain the parameter recommendation scheme includes: Based on the sparse parameter weight matrix, the parameter value range is determined according to environmental constraints and model characteristics, and a parameter search space is constructed based on the parameter value range; Based on the sparse parameter weight matrix and the parameter search space, the pre-screening only considers combinations of non-zero weight parameters to obtain a simplified parameter space. Based on the simplified parameter space, a multi-objective optimization model is constructed, which is used to indicate the simultaneous minimization of inference latency, the maximization of throughput, the maximization of resource utilization efficiency, and the maintenance of specified model accuracy. Based on the multi-objective optimization model, the Pareto optimal solution set is searched to obtain multiple sets of optional parameter configuration data; Based on the multiple sets of optional parameter configuration data and preset user performance preference data, the parameter recommendation scheme is generated.

[0017] As an improvement to the above solution, based on the deployment results and the parameter configuration scheme, the monitoring system data is configured, and the collection indicators and collection frequency are set. Based on the monitoring system data, the collection indicators, and the collection frequency, monitoring configuration data and initial state data are obtained, including: Based on the monitoring system data, the collection indicators, and the collection frequency, multidimensional indicators are obtained, including computing performance indicators, resource utilization indicators, quality indicators, and business indicators. Based on the multidimensional indicators, a hierarchical acquisition strategy is determined, which includes a high-frequency acquisition key delay indicator acquisition strategy, a mid-frequency acquisition resource utilization indicator acquisition strategy, and a low-frequency acquisition quality evaluation indicator acquisition strategy. Based on the hierarchical acquisition strategy, time series data on the multidimensional indicators are collected, and feature engineering processing is performed on the time series data to obtain a monitoring dataset. Based on the monitoring dataset, a multi-component Gaussian mixture model is trained using a preset expectation-maximization algorithm, and the optimal number of components is determined. For each component, the mean vector, covariance matrix, and mixture weights are learned, and the typical working state represented by each component is identified, thus forming a trained multi-component Gaussian mixture model. Based on the trained multi-component Gaussian mixture model, a mapping relationship from components to system states is established, and normal state boundaries are defined. Based on the mapping relationship and the normal state boundaries, monitoring configuration data and initial state data are obtained.

[0018] Secondly, embodiments of this application also provide an integrated processing system for deploying machine learning models, including: The model file acquisition module is used to acquire model files uploaded by users; The parsing module is used to identify the framework type and call the corresponding parser based on the file characteristics of the model file, and to parse the framework model and the parser to determine the model parsing report, wherein the model parsing report includes the model's structured data and dependency information; The scheme configuration module is used to determine the recommended parameter scheme based on the model parsing report and the deployment target environment selected by the user, and to verify the validity of the recommended parameter scheme to obtain the parameter configuration scheme. The environment deployment module is used to generate an environment preparation script and a standardized model file after standardization transformation based on the parameter configuration scheme and the dependency information; generate an interface file based on the model structured data and the parameter configuration scheme; and form an environment deployment configuration file based on the parameter configuration scheme and the deployment target environment. The deployment data construction module is used to construct a deployment data package based on the environment preparation script, the standardized model file, the interface file, and the environment deployment configuration file; The environment data acquisition module is used to acquire the user's target environment credentials, which are used to indicate the environment connection status. The evaluation module is used to perform automated deployment based on the target environment credentials and the deployment data package to obtain deployment results, and to perform operational quality evaluation based on the deployment results and the parameter configuration scheme to obtain service quality evaluation results. Based on the service quality evaluation results, the parameter configuration scheme and the deployment data package are optimized.

[0019] The beneficial effects of this invention are as follows: The integrated processing method and system for machine learning model deployment provided by this invention achieves full-process automation from model file upload to service deployment. It can automatically identify multiple framework models and extract key features, intelligently recommend optimal deployment parameters and verify their effectiveness, automatically generate standardized deployment files and execute deployment, and provide end-to-end monitoring and optimization feedback. Compared with existing technologies, this invention has the following advantages: First, it significantly simplifies the model deployment process, reduces manual intervention, and improves deployment efficiency; second, it achieves unified adaptation to multiple frameworks and environments, lowering the technical threshold; third, it optimizes resource configuration through intelligent parameter recommendation, improving the quality of model services; and fourth, it establishes end-to-end monitoring and closed-loop optimization to ensure the stability of deployed services. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 A flowchart illustrating an integrated processing method for deploying a machine learning model, provided in an embodiment of this application; Figure 2 This is a schematic diagram of the structure of an integrated processing system for deploying machine learning models, provided in an embodiment of this application. Detailed Implementation

[0022] The embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0023] like Figure 1 As shown in the figure, this application embodiment provides an integrated processing method for deploying machine learning models, which includes the following steps S1 to S7.

[0024] S1. Obtain the model file uploaded by the user.

[0025] S2. Based on the file characteristics of the model file, identify the framework type and call the corresponding parser, and parse the framework model and the parser to determine the model parsing report, wherein the model parsing report includes the model structured data and dependency information.

[0026] In this application, the framework type is identified and the corresponding parser is invoked based on the file characteristics of the model file. File characteristics include the file extension (e.g., .pth for PyTorch models, .h5 for Keras models), file header information, and internal structural features. Framework type identification is a fundamental step in model parsing, determining which specific parser should be used to process the model. For some ambiguous scenarios, a trial-and-error loading approach is used for secondary confirmation, i.e., testing the compatibility of the model file using loading functions of different frameworks to ensure correct framework type identification. The parser is a dedicated program component designed for a specific machine learning framework, capable of understanding the framework's model structure and serialization format, extracting the model's internal information, and converting it into a standardized representation.

[0027] After framework type identification and parser selection, the framework model is analyzed in depth to extract structured data and dependency information, generating a complete model analysis report. Structured data includes network structure information (such as the hierarchical topology of the neural network, the type and parameters of each layer), computational characteristics (such as the number of floating-point operations and computational complexity), and feature information (such as the dimensions and data types of inputs and outputs). Dependency information includes framework version dependencies (such as PyTorch 1.10.0), hardware dependencies (such as CUDA 11.3), and environment configuration requirements (such as operating system version and CPU instruction set support). This information constitutes the core content of the model analysis report, providing fundamental data support for subsequent parameter configuration and environment preparation.

[0028] S3. Based on the model analysis report and the deployment target environment selected by the user, determine the parameter recommendation scheme, and verify the validity of the parameter recommendation scheme to obtain the parameter configuration scheme.

[0029] In this application, a recommended parameter scheme is determined based on the model analysis report and the user-selected deployment target environment. The deployment target environment refers to the computing environment in which the model will run, such as a Kubernetes cluster, edge device, or cloud server. First, the model's computational characteristics in the model analysis report are analyzed to assess its resource requirements. Then, the matching degree between the model and the environment is calculated by combining the resource characteristics of the target environment (such as available GPU types and memory capacity). Finally, a recommended scheme for resource configuration parameters (such as the number of CPU cores and GPU memory allocation) and runtime configuration parameters (such as batch size and inference accuracy) is generated based on a parameter optimization model trained on historical deployment data. The parameter optimization model is a machine learning-based predictive model that learns optimal configuration rules by analyzing the relationship between parameter configurations and performance indicators in historical deployment data, and can predict the best running parameters for a new model based on its characteristics.

[0030] This application validates the recommended parameters to ensure they meet resource constraints and performance requirements. Resource constraint checks ensure that the configured resources do not exceed the environment's maximum capacity (e.g., recommended GPU memory does not exceed actual available memory); performance requirement checks ensure that expected performance goals (e.g., latency and throughput requirements) are met given the available resources. The validation process generates validation results and adjustment suggestions; if issues are found, the user is prompted to adjust the corresponding parameters. Finally, static deployment parameters and dynamic runtime parameters are integrated to generate a complete parameter configuration scheme, serving as a crucial input for subsequent deployment processes.

[0031] S4. Based on the parameter configuration scheme and the dependency information, generate an environment preparation script and a standardized model file after standardization transformation; based on the model structured data and the parameter configuration scheme, generate an interface file; and based on the parameter configuration scheme and the deployment target environment, form an environment deployment configuration file.

[0032] In this application, an environment preparation script is generated based on the parameter configuration scheme and dependency information. The environment preparation script is a set of system commands used to install necessary dependency libraries, configure system parameters, and prepare hardware drivers in the target environment. These scripts typically use Shell (Linux environment) or PowerShell (Windows environment) formats to automate the environment initialization process and ensure that the model's runtime environment meets requirements. Simultaneously, the original model files are converted into standardized model files. Standardized model files refer to models represented using a cross-framework common format (such as ONNX, PMML), which offers better cross-platform compatibility and deployment flexibility compared to the original model files. The conversion process selects an appropriate intermediate representation format based on the target environment and utilizes corresponding conversion tools (such as PyTorch's torch.onnx.export) to complete the conversion.

[0033] Furthermore, this application generates interface files based on the model's structured data and parameter configuration scheme. The interface files define the API specifications for the model service, including request formats, response formats, and error handling mechanisms, enabling external systems to call the model service in a standardized manner. Interface files typically use RESTful API or gRPC format and include interface definitions (such as Swagger documentation) and interface implementation code (such as Flask / FastAPI server code). Based on the parameter configuration scheme and the target deployment environment, an environment deployment configuration file is formed. The environment deployment configuration file is a configuration specification for a specific deployment environment, such as a Kubernetes YAML file or a Docker Compose file, which defines deployment details such as service resource allocation, network settings, and scaling strategies.

[0034] S5. Based on the environment preparation script, the standardized model file, the interface file, and the environment deployment configuration file, a deployment data package is constructed.

[0035] In this application, a complete deployment package is constructed based on environment preparation scripts, standardized model files, interface files, and environment deployment configuration files. The deployment package is a structured collection of files containing all components required for model deployment, typically in compressed package form. A standardized directory structure is created, various files are placed in their corresponding directories, and container build files (such as Dockerfiles) are generated. Finally, a compression and packaging operation is performed to form the final deployment package. The standardized structure of the deployment package enables the deployment process to be executed consistently across different environments.

[0036] S6. Obtain the user's target environment credential, which is used to indicate the environment connection status.

[0037] In this application, obtaining the user's target environment credentials is a prerequisite for performing automated deployment. Target environment credentials are secure authentication information required to access and control the deployment environment, such as a Kubernetes API Token, a cloud service AccessKey, or an SSH key. These credentials are used to establish a secure connection with the deployment environment, verify access permissions, and obtain the environment connection status, ensuring that subsequent deployment operations can be executed smoothly.

[0038] S7. Based on the target environment credentials and the deployment data package, perform automated deployment to obtain deployment results, and perform operational quality assessment based on the deployment results and the parameter configuration scheme to obtain service quality assessment results. Based on the service quality assessment results, optimize the parameter configuration scheme and the deployment data package.

[0039] In this application, automated deployment is performed based on target environment credentials and deployment data packages to obtain deployment results. The automated deployment process includes steps such as unpacking deployment files, executing environment preparation scripts, building container images (if applicable), submitting deployment configurations to the target environment, and starting service instances. These steps are completed automatically by calling the corresponding environment APIs (such as Kubernetes API, Docker API), requiring no manual intervention, which greatly improves deployment efficiency and consistency. The deployment results include information such as the service's running status, access endpoints, and initial performance metrics.

[0040] Based on the deployment results and parameter configuration scheme, an operational quality assessment is conducted to obtain service quality assessment results. The operational quality assessment is a comprehensive test of the performance and stability of the deployed model service, including computational performance assessment (such as latency and throughput), resource utilization assessment (such as CPU / GPU utilization and memory usage), and model quality assessment (such as accuracy and drift). The assessment process involves configuring a monitoring system (such as Prometheus or Grafana), setting collection metrics and frequencies, collecting service operation data, comparing and analyzing it against expected targets, and generating service quality assessment results.

[0041] Finally, based on the service quality assessment results, the parameter configuration scheme and deployment data package are optimized. Performance anomalies and quality deviations, such as excessive latency, insufficient throughput, or low resource utilization, are detected, generating alarm information and optimization suggestions. Optimization suggestions may include adjusting resource allocation, modifying batch size, and switching inference precision. The parameter configuration scheme is updated according to the optimization suggestions, and the deployment data package is regenerated if necessary, potentially triggering a redeployment process, forming a closed-loop feedback mechanism to continuously optimize model service performance. This adaptive optimization capability maintains optimal operating conditions under varying loads and environmental conditions.

[0042] In some embodiments, the step of identifying the framework type and invoking the corresponding parser based on the file characteristics of the model file, and parsing the framework model and the parser to determine the model parsing report includes: The model file is subjected to security verification to obtain verification results, which include file type verification results, size limit check results, and virus scan results. After the verification result is passed, the model file is saved to a temporary storage area to generate a corresponding identifier, and the model file path and model meta-information are obtained. Based on the model file path and the model meta information, the initial judgment frame type is identified by the file extension, and the initial judgment frame type is identified by a preset scene loading model. The corresponding parser is selected to obtain the frame type and the parser instance corresponding to the parser. The scene loading model is used to indicate the loading of secondary confirmation in a preset fuzzy scene. Based on the framework type and the parser instance, model structured data is generated, which includes network structure, computational characteristic information, and feature information. Based on the model file and the framework type, the model meta information is scanned to extract framework version dependencies, hardware dependencies, and environment configuration requirements, and corresponding dependency information is generated. Based on the structured data of the model and the dependency information, a model parsing report is generated. The step of generating structured model data based on the framework type and the parser instance includes: Based on the framework type and the parser instance, a preset framework API is called to extract network structure information, which includes network layer type, input and output tensor shape, and number of parameters. Based on the network structure information, the computational characteristics of the model are determined, including the number of floating-point operations and computational complexity. Based on the network structure information, the feature information of the model is extracted, including the input feature dimension, the output feature dimension, and the feature type. Based on the network structure information, the computational characteristic information, and the feature information, the model structured data is generated.

[0043] First, the model file undergoes security verification to obtain comprehensive verification results. Security verification is a necessary step to ensure that the model file is legitimate, secure, and meets system requirements. It typically includes three key aspects: file type verification results, size limit check results, and virus scan results. File type verification is the process of verifying whether the model file format meets expectations. It confirms the true type of the file by checking the file header, magic number, or internal structural features. This step prevents file type spoofing, which involves modifying the file extension to mask the true type of the file. File type verification typically uses various techniques, including: magic number checking (checking the sequence of identifying bytes at specific positions in the file header), content analysis (scanning the file content to find characteristic patterns of specific frames), and structural verification (attempting to parse the file according to a specific format to verify whether its internal structure meets expectations).

[0044] Size limit checks ensure that the model file size is within the system's supported range. Excessively large model files may exceed system processing capacity or consume excessive resources, while abnormally small files may indicate an incomplete or corrupted model. Size limit checks compare the actual file size with the system's set minimum and maximum thresholds to ensure the file size is reasonable. This check may also include a preliminary assessment of model complexity, such as roughly estimating whether the number of model parameters is within a reasonable range based on the file size.

[0045] Virus scanning is the process of detecting malicious code or security threats in model files. While machine learning model files are typically data structures, modern model formats (especially those that support custom operations) may contain executable code or implanted malicious content. Virus scanning may employ various security techniques, such as signature matching (matching against a database of known virus signatures), behavioral analysis (analyzing possible anomalous behavioral patterns in the file), and sandbox execution (securely loading the model in an isolated environment to detect abnormal behavior). In high-security environments, deep inspection may also be performed, such as checking custom operations within the model for potential malicious behavior.

[0046] In this embodiment, the security verification result is a comprehensive report, including the results of each check and a possible risk assessment. Only when all security checks pass does the next step of processing begin; if any security issues are detected, detailed information is recorded, the processing flow is terminated, and the corresponding error message and suggestions are returned to the user.

[0047] After the verification result passes, the model file is saved to a temporary storage area to generate the corresponding identifier, obtaining the model file path and model metadata. The temporary storage area is a dedicated system area for storing files to be processed. It typically has an automatic lifecycle management mechanism, automatically cleaning up files after processing is complete or after a timeout. Storing files in a temporary area instead of processing them directly has several advantages: it provides a stable file reference path for easy access in subsequent processing steps; it isolates file operations, reducing dependence on the original file; and it also provides a foundation for parallel processing, allowing multiple parsing tasks to run concurrently.

[0048] An identifier is a unique identifier generated for each model file, typically a hash value (such as SHA-256) or a UUID (Universally Unique Identifier), used to uniquely identify the model file throughout the processing flow. This identifier is used not only for file references but also frequently for associating processing results and caching queries. The model file path is the complete access path to the file in the temporary storage area, used by subsequent processing steps.

[0049] Model metadata refers to the basic attribute data of model files, including but not limited to: file size, creation time, last modification time, owner information, access permissions, and preliminary information extracted from the filename or path (such as possible frame type hints). In addition, preliminary summary information of the file content is extracted, such as file header features and format version identifiers. This metadata provides initial clues for subsequent frame identification and parsing, and also serves as basic data recorded in the system for model management.

[0050] Next, based on the model file path and model metadata, the initial framework type is identified by the file extension. The model is then loaded using a preset scenario to verify the initial framework type, and the appropriate parser is selected to obtain the accurate framework type and the corresponding parser instance. Framework type identification is the process of determining the machine learning framework used by the model (such as TensorFlow, PyTorch, ONNX, MXNet, etc.), and this information is crucial for selecting the correct parsing strategy.

[0051] Initial framework type determination is typically based on file extensions (e.g., .pb might point to TensorFlow, .pt might point to PyTorch), but this method is unreliable because file extensions can be incorrectly set or intentionally modified. Therefore, a multi-layered framework identification strategy is adopted. First, file extension identification serves as a preliminary judgment, providing initial candidates for the framework type; then, the system uses a more reliable scene loading model for secondary confirmation.

[0052] Scenario-based model loading is a technique that attempts to load a model in a pre-defined, ambiguous scenario (i.e., when the exact type of the model is uncertain). This process involves creating a minimal loading environment for each possible framework type, attempting to load the model file according to that framework's standard methods, and observing whether it can be successfully parsed. For example, for a file that might be a TensorFlow model, the system might try loading it using the TensorFlow API; for a file that might be a PyTorch model, it might try using PyTorch's methods. This approach is more reliable because it directly tests the model's compatibility with the framework, rather than relying solely on the filename or header features. In some advanced implementations, it may even be possible to try multiple framework loading methods in parallel, selecting the framework that loads successfully and produces the most complete parsed result as the final decision.

[0053] Based on the recognition results, the appropriate parser is selected and instantiated. A parser is a software component specifically designed to parse model files from a particular framework, understanding its file format, data structure, and characteristics. Each mainstream machine learning framework typically corresponds to a dedicated parser, such as the TensorFlow parser, PyTorch parser, and ONNX parser. A parser instance is a concrete object of the parser class, configured with specific parameters and states, ready to parse the current model file. The choice of parser directly determines the subsequent parsing method and the types of information that can be extracted.

[0054] Based on a defined framework type and parser instance, structured model data is generated. This is a detailed description of the model's internal structure and characteristics, typically including network structure, computational characteristics, and feature information. The network structure is the skeleton of the model, describing the organization and connections of layers or operations within the model. It is usually represented as a computation graph or hierarchical structure, where nodes represent computational operations (such as convolution, pooling, and fully connected layers), and edges represent data flow. Network structure information includes the type of each layer, parameter configurations (such as kernel size and activation function type), and input / output shapes. Parsing the network structure involves traversing the operation definitions in the model file, constructing a complete computation graph representation, and extracting the detailed configuration of each operation node.

[0055] Computational characteristics describe the model's computational requirements and performance features, including the total number of parameters (the total number of variables that need to be trained or inferred in the model), computational cost (usually expressed as floating-point operations (FLOPS), reflecting the model's computational complexity), memory requirements (the amount of memory required at runtime), and computationally intensive regions (the parts of the model with particularly high computational demands). This information is crucial for subsequent performance optimization and resource planning. Computational characteristic analysis typically requires traversing the network structure, calculating the theoretical computational cost and memory requirements for each operation based on its type and configuration, and then summing them to obtain the overall metrics.

[0056] Feature information focuses on the model's specific characteristics or unique aspects, such as quantization schemes (whether the model uses quantization techniques like int8 or float16), special operations (non-standard or custom operations used in the model), parallel strategies (parallel computation mechanisms in the model design), and framework-specific features (parts that utilize features unique to a particular framework). This feature information helps identify the model's optimization space and deployment challenges. Feature extraction typically requires framework-specific knowledge and is accomplished by a dedicated parser module.

[0057] In parallel with model structure parsing, based on the model file and the determined framework type, model metadata is scanned to extract framework version dependencies, hardware dependencies, and environment configuration requirements, generating corresponding dependency information. Dependency information describes the external conditions and resources required for the model to function correctly, and is crucial for ensuring deployment environment compatibility.

[0058] Framework version dependencies specify the range of framework versions required by the model, such as "requires TensorFlow 2.3.0 or later". Models may use features introduced in a specific version or depend on the behavior of a specific version, making version compatibility crucial for proper operation. Version dependencies can be extracted based on explicit tags in the model file or inferred by analyzing the operations and functions used by the model.

[0059] Hardware dependencies describe a model's specific requirements for hardware resources, such as "requires an NVIDIA GPU that supports CUDA" or "requires a CPU that supports the AVX instruction set." Some models may contain code optimized for specific hardware or operations that depend on specific hardware features. Identification of hardware dependencies is typically based on the specific types of operations or optimization tags used in the model, as well as the framework's known hardware requirements.

[0060] Environment configuration requirements include the configuration needs of the runtime environment, such as necessary environment variables, external library dependencies, and memory configuration recommendations. These requirements may affect model loading, initialization, and execution. Environment configuration requirements may be extracted from model metadata, findings during the parsing process, or inferences based on empirical rules derived from framework type and model characteristics.

[0061] In this embodiment, the dependency information generation process comprehensively considers the model file content, framework characteristics, and system experience base, providing a detailed description for each dependency as much as possible, including version range, compatibility notes, and possible alternatives. This information will help the deployment team prepare an appropriate runtime environment and avoid compatibility issues.

[0062] Finally, based on the generated structured model data and dependency information, a complete model analysis report is compiled. The model analysis report is a comprehensive document providing a complete view of the model, including its structure, characteristics, requirements, and limitations. The report typically uses a structured format (such as JSON or XML) to facilitate subsequent processing and analysis. The report integration process involves more than just simple data combination; it may also include additional analysis and evaluation, such as performance predictions, optimization suggestions, or potential problem alerts. A typical model analysis report may include the following main sections: basic information (model name, source, purpose, creation time, etc.), structural overview (network layers, main components, computation paths, etc.), detailed structure (detailed configuration and connections for each layer), computational resource requirements (number of parameters, computational load, memory requirements, etc.), dependencies (framework version, hardware requirements, environment configuration, etc.), special characteristics (quantization schemes, custom operations, parallel strategies, etc.), potential challenges (identified deployment difficulties or optimization opportunities), and compatibility assessment (expected compatibility with different deployment environments).

[0063] Optionally, the final stage of report generation may also include visualization processing, such as generating network structure diagrams, computational distribution diagrams, or dependency graphs, to help users intuitively understand the model's characteristics. The final model analysis report will serve as an important reference document in the model deployment process, guiding subsequent optimization, adaptation, and deployment decisions.

[0064] In this embodiment, the entire model parsing process demonstrates a deep understanding and systematic processing of the machine learning model. Basic security is ensured through security checks, the accuracy of parser selection is guaranteed through multi-level framework identification, and a comprehensive view of the model is provided through deep structural parsing and dependency analysis. This process not only provides technical implementation details but also lays a solid foundation for model deployment and optimization, enabling the model to run efficiently, securely, and stably in a production environment.

[0065] Therefore, the quality of model parsing directly impacts the success rate and efficiency of subsequent deployments. Thus, each step incorporates multi-layered verification and robustness measures to handle various anomalies such as format errors, missing data, or unsupported features. This embodiment also maintains complete logging, recording key decisions and findings during the parsing process, facilitating troubleshooting and process optimization. This systematic, multi-layered parsing method provides an accurate and comprehensive model view, even with complex and ever-changing model files, offering a reliable basis for intelligent deployment decisions.

[0066] More specifically, firstly, based on a defined framework type and parser instance, the pre-defined framework API is invoked to extract network structure information. Network structure information is core data describing the internal organization and connections of a neural network, including key elements such as network layer types, input / output tensor shapes, and the number of parameters. Framework APIs (Application Programming Interfaces) are programming interfaces provided by various machine learning frameworks, allowing developers to programmatically access and manipulate framework functions, including model loading, structure analysis, and inference execution. Different frameworks provide different APIs, such as TensorFlow's SavedModel API, PyTorch's JIT module, or ONNX's Model API, but they all allow programmatic inspection of the model structure.

[0067] The process of extracting network structure by calling a framework's API typically involves several key steps: First, model loading, using the framework's loading function to read the model file into memory and create a framework-specific model representation; second, structure traversal, traversing the model's computation graph or hierarchy to identify each operation node or layer; third, information extraction, extracting detailed information from each node or layer; and finally, structure reconstruction, reorganizing the extracted information into a unified structural representation for easier subsequent processing. This process needs to handle the peculiarities of different frameworks and various exceptional cases, such as complex situations like custom operations, nested structures, or conditional branches.

[0068] Network layer types refer to the functional categories of each layer in a neural network, such as convolutional layers, pooling layers, fully connected layers, activation layers, normalization layers, and recurrent layers. Each type of layer performs specific mathematical operations, collectively constituting the computational power of the neural network. Identifying network layer types is usually done by analyzing the names, attributes, or computation patterns of the operation nodes. Some frameworks (such as ONNX) directly store operation type information in the model, while other frameworks may require more complex inference.

[0069] The shape of input and output tensors describes the dimensional structure of data as it passes between layers of a network. A tensor is a multidimensional array in machine learning and is the basic unit of data processed by neural networks. Tensor shapes are typically represented as a tuple of integers, such as (batch_size, height, width, channels) for image data, or (batch_size, sequence_length, embedding_dimension) for sequence data. Understanding tensor shapes is crucial for understanding data flow and memory requirements. Extracting tensor shapes may involve static analysis (read directly from the model definition) or dynamic inference (computed by simulating the data flow). For some frameworks (such as PyTorch's dynamic graph mode), it may be necessary to provide sample inputs to determine accurate shape information.

[0070] The number of parameters is the total number of variables that need to be learned or stored in a model, and it is an important metric for measuring model complexity and storage requirements. Parameters include weights and biases, which determine the model's behavior and performance. Calculating the number of parameters typically involves iterating through all the parameter variables in the model and summing the number of elements for each variable. For complex models, it may be necessary to consider situations such as parameter sharing (multiple operations using the same set of parameters) and conditional branching (some parameters are only used under specific conditions).

[0071] During the extraction of network structure information, a computational graph representation is constructed. This is a graph data structure where nodes represent operations and edges represent data flow. The computational graph representation enables the system to analyze data dependencies, identify opportunities for parallel execution, and optimize repetitive computations. For some frameworks (such as TensorFlow), the computational graph is explicitly stored; while for other frameworks (such as PyTorch's dynamic graph mode), it may be necessary to construct the graph representation by tracing the execution process.

[0072] Next, based on the extracted network structure information, the computational characteristics of the model are determined, mainly including the number of floating-point operations and computational complexity. These computational characteristics describe the model's computational requirements and performance features, serving as a crucial basis for resource planning and performance optimization.

[0073] Floating-point operations (FLOPs) are a common metric for measuring the computational complexity of a model, representing the total number of floating-point mathematical operations required for the model to complete one forward propagation (or inference). FLOPs calculation typically focuses on multiplication and addition operations, with different calculation methods for different types of network layers. For example, for fully connected layers, FLOPs are usually the product of the number of input neurons and the number of output neurons, multiplied by 2 (considering multiplication and addition); for convolutional layers, the calculation is more complex, requiring consideration of factors such as kernel size, number of input and output channels, and feature map size. FLOPs analysis can identify computationally intensive regions in the model, guiding optimization directions, such as prioritizing quantization or pruning high-FLOPs layers.

[0074] Computational complexity is a theoretical analysis of a model's computational requirements, typically expressed using Big O notation (e.g., O(n²), O(n·logn)) to represent how an algorithm's performance changes with increasing input size. In neural networks, computational complexity analysis usually considers the time complexity of key operations, such as matrix multiplication, convolution, or attention mechanisms. Computational complexity analysis helps understand the model's performance bottlenecks under different input sizes, guiding architecture optimization and algorithm selection. Unlike FLOPs, computational complexity focuses more on the theoretical efficiency of the algorithm than on the specific number of operations.

[0075] The determination of computational characteristics is typically accomplished through theoretical analysis and algorithmic modeling. This embodiment traverses the network structure, applies the corresponding computational model to each layer or operation, and then sums the results to obtain the overall index. For complex network structures (such as those with conditional branches or dynamic shapes), statistical methods or worst-case analysis may be necessary. Modern analytical tools usually have built-in libraries of computational models for various common operations, enabling them to perform this analysis automatically.

[0076] In determining computational characteristics, memory access patterns and cache utilization may also be analyzed, as these factors can have a greater impact on performance on actual hardware than the raw FLOPs. For example, some operations, while having low FLOPs, may have discontinuous memory accesses, leading to high cache miss rates and slower execution; while other operations, although having high FLOPs, may exhibit good data locality and perform better on actual hardware. This in-depth analysis helps to more accurately predict the model's performance on specific hardware platforms.

[0077] In parallel, based on network structure information, feature information of the model is extracted, including input feature dimensions, output feature dimensions, and feature types. Feature information describes the characteristics of the data processed by the model and is crucial for understanding the model's application scenarios and data requirements.

[0078] Input feature dimensions describe the dimensional structure of the model's input data, i.e., the shape of the data the model expects to receive. For image models, input feature dimensions might be a four-dimensional tensor (batch_size, height, width, channels); for text models, they might be two-dimensional or three-dimensional tensors such as (batch_size, sequence_length) or (batch_size, sequence_length, embedding_dim); for time series models, they might be in the form of (batch_size, time_steps, features). Accurately understanding input feature dimensions is crucial for data preprocessing and model interface design. Input feature dimensions can typically be extracted directly from the model's input layer or input placeholders.

[0079] Output feature dimensions describe the dimensional structure of the predictions or results generated by the model. For classification models, the output might be a two-dimensional tensor of the form (batch_size, num_classes); for image generation models, it might have the same dimensions as the input image; for object detection models, the output might be a complex multidimensional structure containing bounding box coordinates, class, and confidence scores. Understanding output feature dimensions is crucial for correctly interpreting model results and designing post-processing logic. Output feature dimensions are typically extracted from the model's output layer or final operation.

[0080] Feature type refers to the type of data processed by the model and its semantic meaning. Common feature types include images (which may be further subdivided into grayscale images, RGB images, etc.), text (which may be raw text, word embeddings, or character embeddings), audio (which may be raw waveforms, spectrograms, or MFCC features), and numerical data (such as sensor data, user behavior data), etc. Feature type identification may be based on model structural features (such as convolutional layers typically used for image processing, and recurrent layers often used for sequence data), preprocessing operations, or model metadata. Understanding feature types helps determine appropriate data preprocessing and post-processing methods, as well as assess the model's compatibility with specific application scenarios.

[0081] Feature extraction may involve various techniques, including static analysis (analyzing model definition and structure), example inference (tracking data flow using example inputs), and metadata parsing (reading descriptive information accompanying the model). For some models, especially optimized or compressed models, a combination of methods may be necessary to accurately infer feature information.

[0082] Finally, based on the extracted network structure information, computational characteristic information, and feature information, complete structured model data is generated. This step integrates the various types of information obtained earlier into a unified structured representation, facilitating subsequent analysis and processing.

[0083] Structured data for models typically employs hierarchical data structures, such as nested JSON or XML formats, to organize all extracted information. A typical structure may include several main parts: basic information (model name, framework type, version, etc.), network structure (hierarchical relationships, connection patterns, etc.), computational characteristics (FLOPs, complexity, memory requirements, etc.), and feature information (input / output dimensions, data types, etc.).

[0084] The process of generating structured data is not just a simple combination of information; it may also involve additional processing and analysis: data standardization (converting specific representations of different frameworks into a unified format), relational modeling (establishing reference relationships between different elements, such as the correspondence between layers and parameters), integrity checks (verifying that all necessary information has been extracted), and consistency checks (ensuring that information from different sources is consistent with each other).

[0085] The generated structured model data provides a comprehensive view of the model and supports a variety of subsequent applications: it can be input into visualization tools to generate model architecture diagrams and data flow diagrams; it can be used in performance prediction tools to estimate the model's performance on specific hardware; it can support automated system optimization, identify optimization opportunities, and apply appropriate techniques; and it can also be used for compatibility analysis to assess the model's suitability for different deployment environments.

[0086] Structured model data also provides a foundation for knowledge accumulation and model management. By storing structured data from different models in a database, organizations can build model knowledge bases, supporting model retrieval, comparison, and experience accumulation. For example, they can analyze the performance patterns of specific types of networks on different hardware platforms, or identify the correlation between common structural features and deployment challenges, providing guidance for future model design and optimization.

[0087] In the process of generating structured data for models, multiple validations are typically applied to ensure the accuracy and integrity of the data. This includes cross-validation with the original model specifications, consistency checks with simple inference tests, and special review of outliers and atypical structures. For complex or non-standard models, confidence markers may be generated to indicate the level of certainty of specific information, helping downstream systems to properly handle uncertainty.

[0088] Therefore, the systematic and multi-layered model structure extraction and integration process transforms complex model files into clearly structured and information-rich data representations, laying a solid foundation for subsequent model understanding, optimization, and deployment. Through this method, even the most complex neural network models can be decomposed into understandable and analyzable components, making the model deployment process more controllable, efficient, and optimizable. Structured model data, as the core output of model parsing, connects the model development and deployment stages, serving as a crucial bridge for achieving efficient model production.

[0089] In some embodiments, the step of determining a parameter recommendation scheme based on the model parsing report and the user-selected deployment target environment, and validating the parameter recommendation scheme to obtain a parameter configuration scheme, includes: Based on the model analysis report, analyze the configurable parameter items; Based on the preset model type and preset framework characteristics, a configuration interface is generated, and the default values ​​and optional ranges are displayed on the configuration interface. Based on the configurable parameter items, the configuration interface, the default values ​​and the optional ranges, an interactive parameter configuration interface is generated and displayed. The system obtains the deployment target environment selected by the user on the interactive parameter configuration interface, and obtains the environment resource characteristics, including GPU type and memory capacity. Based on the target deployment environment and the characteristics of the environmental resources, the environmental constraints are queried to obtain the corresponding deployment environment information; Based on the model analysis report and the deployment environment information, the matching degree between the model's computational characteristics and the environment's resource characteristics is analyzed using a preset parameter optimization model, and configuration parameters are predicted. Based on the model's computational characteristics, the matching degree, and the predicted configuration parameters, a parameter recommendation scheme is obtained, wherein the configuration parameters include resource configuration parameters and runtime configuration parameters. Based on the parameter recommendation scheme, the configuration parameters are verified to meet resource constraints and verification performance requirements, corresponding verification results are generated, and adjustment suggestions are generated based on the verification results to obtain target parameter configuration data. Based on the target parameter configuration data, static deployment parameters and dynamic operation parameters are integrated to generate a parameter configuration scheme.

[0090] In this embodiment, after the analysis is completed, a configuration interface is generated based on the preset model type and preset framework characteristics. The model type categorizes machine learning models by function and structure, such as image classification models, natural language processing models, and recommendation system models. Framework characteristics refer to the unique features and configuration requirements of different machine learning frameworks (such as PyTorch and TensorFlow). Based on the model type and framework characteristics, an appropriate configuration interface template is selected from a predefined interface template library to ensure that the interface includes the most relevant parameters for that model type. The configuration interface displays the default value and optional range for each parameter. The default value is the initial configuration preset by the system based on general best practices, while the optional range defines the valid value range for the parameter, preventing users from setting invalid or dangerous values. For example, the default value for batch size might be 16, with an optional range of 1 to 128; the default value for GPU memory allocation might be 1.5 times the estimated model requirement, with a range from the minimum requirement to the total available memory.

[0091] Based on configurable parameters, the configuration interface, default values, and optional ranges, an interactive parameter configuration interface is generated and displayed to the user. This interactive parameter configuration interface is a dynamic user interaction component that allows users to intuitively view, adjust, and verify configuration parameters. Such interfaces typically take the form of web forms, JSON editors, or graphical control panels, and feature automatic updates of parameter dependencies, real-time verification, and intelligent suggestions, improving the efficiency and accuracy of user configuration. Through this interface, users can adjust various parameters according to their specific needs while receiving immediate feedback from the system.

[0092] On the interactive parameter configuration interface, users first need to select the target deployment environment. This is the computing environment where the model will ultimately run, which could be a public cloud platform (such as AWS or Azure), a private Kubernetes cluster, an edge device, or a local server. After selecting the environment, its resource characteristics are automatically retrieved. Environment resource characteristics are details of the computing resources available in the target environment, with particular attention to GPU type (such as NVIDIA T4 or V100, which have different computing capabilities and architectural features) and memory capacity (such as the size of system memory and GPU video memory). These factors directly affect the performance and capacity limitations of the model. Resource characteristics are typically obtained through environment API queries or retrieved from a pre-registered environment configuration database.

[0093] Based on the selected target deployment environment and the acquired environmental resource characteristics, the system queries environmental constraints to obtain complete deployment environment information. Environmental constraints are the limitations and rules of the target environment, including resource quotas (the maximum amount of resources that can be allocated to each service), scheduling policies (the priority and rules for resource allocation), network policies (allowed communication modes and bandwidth), etc. These conditions may come from an environment management system (such as Kubernetes' ResourceQuota) or policies set by the administrator. Deployment environment information is a comprehensive description of the environmental resource characteristics and constraints, providing an environmental context for parameter configuration.

[0094] Next, based on the model analysis report and deployment environment information, a pre-defined parameter optimization model is used for analysis and prediction. The parameter optimization model is a machine learning model that learns optimal configuration rules by analyzing the relationship between parameter configurations and performance metrics in historical deployment cases. It can be an algorithm based on gradient boosting trees, neural networks, or Bayesian optimization, capable of predicting the best parameter configuration based on the characteristics of the new model and environmental conditions. First, the matching degree between the model's computational characteristics and the characteristics of environmental resources is analyzed. The matching degree is a comprehensive score reflecting the degree of fit between the model's needs and environmental resources, such as the ratio of the model's required GPU computing power to the GPU performance provided by the environment, and the ratio of the model's memory requirements to available memory. A high matching degree means that environmental resources fully meet the model's needs, while a low matching degree indicates a potential performance bottleneck.

[0095] Based on matching degree analysis, the parameter optimization model predicts configuration parameters, including resource configuration parameters and runtime configuration parameters. Resource configuration parameters determine the amount of computing resources allocated to the model service, such as the number of CPU cores, GPU memory limits, and network bandwidth. Runtime configuration parameters control the model's runtime behavior, such as batch size (the number of samples processed at once), inference accuracy (e.g., FP32, FP16, or INT8), and concurrency (the number of requests processed simultaneously). The prediction process considers various factors, including model characteristics (such as computational complexity and memory access patterns), environmental conditions (such as GPU architecture and memory bandwidth), and performance goals (such as minimum latency and maximum throughput), to generate the optimal configuration combination. By combining the model's computational characteristics, matching degree score, and predicted configuration parameters, a complete parameter recommendation scheme is formed, providing users with professional configuration suggestions.

[0096] To ensure the feasibility and effectiveness of the recommended solution, configuration parameters are validated. The validation process includes two aspects: first, checking whether the configuration parameters meet resource constraints to ensure that requested resources do not exceed environmental limits and avoid deployment failure; second, evaluating whether performance requirements can be met under a given configuration, such as whether inference latency is within acceptable limits and whether throughput meets expectations. Validation may use a rule engine to check configuration validity or a performance prediction model to evaluate expected results. Validation results include pass, warning, and fail items. Based on the validation results, adjustment suggestions are generated, such as "It is recommended to increase GPU memory allocation by at least 4GB" or "Excessive batch size may cause latency to exceed limits; it is recommended to reduce it to below 16." Users can adjust parameters according to these suggestions until satisfactory target parameter configuration data is obtained.

[0097] Finally, based on the target parameter configuration data, static deployment parameters and dynamic runtime parameters are integrated to generate the final parameter configuration scheme. Static deployment parameters are configurations determined at deployment time and remain unchanged during runtime, such as resource allocation, environment variables, and startup parameters; dynamic runtime parameters are configurations that can be adjusted during service operation, such as batch size, caching strategy, and concurrency control. The parameter configuration scheme is a structured collection of configurations, typically represented in JSON or YAML format, containing all parameter settings required for model deployment and operation. This scheme will serve as an important input for subsequent deployment processes, guiding environment preparation, model service configuration, and runtime behavior.

[0098] In this embodiment, model characteristic analysis and environmental resource assessment are fully combined. The optimal configuration scheme is generated through machine learning optimization technology, and an interactive interface and intelligent verification mechanism are provided. This ensures the scientificity and effectiveness of the configuration while taking into account the specific needs and control flexibility of users. It is an indispensable key link in the integrated model deployment process.

[0099] In some embodiments, the process of generating an environment preparation script and a standardized model file after standardization transformation based on the parameter configuration scheme and the dependency information, generating an interface file based on the model structured data and the parameter configuration scheme, and forming an environment deployment configuration file based on the parameter configuration scheme and the target deployment environment includes: Based on the dependency information and parameter configuration scheme in the model parsing report, the environment preparation script is generated by initializing the environment script and then encapsulating it. Based on the model parsing report and the parameter configuration scheme, an intermediate representation format suitable for the target environment is selected, and the model file is converted into a standardized model file according to the intermediate representation format using a preset call conversion model. Based on the input and output feature information in the model analysis report and the parameter configuration scheme, define the service API interface and generate the corresponding interface file based on the service API interface; Based on the parameter configuration scheme and the target deployment environment, select the corresponding environment deployment template, and form an environment deployment configuration file according to the environment deployment template.

[0100] In the machine learning model deployment process, after the parameter configuration scheme is determined, various files required for actual deployment need to be generated. The first step is to generate an environment preparation script based on the parameter configuration scheme and dependency information. This script is a set of system commands or executable code used to automatically set up the hardware and software environment required for model operation, ensuring that dependent libraries, drivers, and system configurations meet the requirements. Based on the dependency information (including framework version, library dependencies, and hardware requirements) and parameter configuration scheme in the model parsing report, the environment script is initialized and encapsulated. The initialization process first determines the target environment's operating system type (e.g., Linux, Windows) and selects the appropriate scripting language (e.g., Shell, PowerShell). Then, installation commands are generated based on the dependency information, such as using apt-get, pip, or conda to install necessary packages. Next, commands for setting environment variables, adjusting system parameters, and configuring hardware drivers are added. Finally, these commands are encapsulated into a structured script, with error handling, logging, and verification steps added to ensure the reliability and traceability of script execution. The encapsulated environment preparation script typically consists of four main parts: pre-checks, dependency installation, environment configuration, and verification testing, forming a complete environment preparation process.

[0101] Simultaneously, based on the model parsing report and parameter configuration scheme, the original model file needs to be converted into a standardized model file. The standardized model file uses a cross-framework universal format, offering better cross-platform compatibility and deployment flexibility compared to the original framework-specific format. The conversion process first selects an intermediate representation format suitable for the target environment. An intermediate representation format is a standardized model representation method, such as ONNX (Open Neural Network Exchange), PMML (Predictive Model Markup Language), or TensorRT. ONNX is an open format supporting model exchange between different deep learning frameworks; PMML focuses on representing traditional machine learning models; and TensorRT is NVIDIA's high-performance inference engine format optimized for GPUs. The choice of format depends on the model type, target hardware, and performance requirements. After determining the format, the original model is converted into a standardized model using a pre-defined conversion model. The conversion model refers to the tool or API responsible for format conversion, such as PyTorch's `torch.onnx.export()` or TensorFlow's `tf.saved_model`. The conversion process preserves the model's computation graph structure, weight parameters, and metadata information, and may also perform optimizations such as operator fusion and weight quantization to improve inference efficiency.

[0102] Next, based on the input / output feature information and parameter configuration scheme in the model parsing report, interface files are generated. These interface files define the API specifications of the model service, enabling external systems to call the model service in a standardized manner. Defining the service API interface is the core of this process; it includes determining the interface protocol (e.g., REST, gRPC), designing request and response formats, planning interface paths and methods, and formulating error handling strategies. For example, an image classification model's API might accept Base64-encoded image data and return class labels and confidence scores; a text processing model might accept JSON-formatted text fields and return processing results and related analyses. Based on the model's input / output features (e.g., dimensions, data types) and user settings in the parameter configuration scheme (e.g., batch size, response format), an adapted API definition is automatically generated. Based on the service API interface, the system generates corresponding interface files, including interface definition documents (e.g., Swagger / OpenAPI specifications), a client SDK (for easy user integration), and server implementation code (e.g., Flask / FastAPI applications). These interface files enable seamless integration of the model service with external systems, providing standardized access methods.

[0103] Finally, based on the parameter configuration scheme and the target deployment environment, a corresponding environment deployment template is selected to generate the environment deployment configuration file. The environment deployment configuration file is a configuration specification for a specific deployment environment, defining deployment details such as service resource allocation, network settings, and scaling strategies. The environment deployment template is a predefined configuration file skeleton, designed for the specific format and requirements of different deployment platforms (such as Kubernetes, Docker Swarm, AWS ECS, etc.). A suitable template is selected based on the target deployment environment, such as a Kubernetes YAML file, a Docker Compose file, or a cloud service configuration JSON. After selecting a template, the system fills in the resource configuration (such as CPU / GPU allocation, memory limits), runtime parameters (such as the number of instances, auto-scaling strategy), and network settings (such as port mapping, load balancing) from the parameter configuration scheme into the corresponding fields, generating a complete environment deployment configuration file. These configuration files will guide the deployment system on how to create, configure, and manage model service instances.

[0104] After generating the environment preparation scripts, standardized model files, interface files, and environment deployment configuration files, they need to be organized into a structured deployment package. The deployment package is a collection of files containing all the components required for deployment, facilitating transmission, storage, and execution of deployment operations. The build process first creates a deployment package directory structure, a standardized file and directory hierarchy, including a script directory (containing environment preparation scripts), a model directory (containing standardized model files), an interface directory (containing API interface files), and a configuration directory (containing deployment configuration files), etc. This standardized directory structure ensures that deployment tools can consistently locate and use various types of files, improving the automation and reliability of the deployment process.

[0105] In some embodiments, the step of constructing a deployment data package based on the environment preparation script, the standardized model file, the interface file, and the environment deployment configuration file includes: Based on the environment preparation script, the standardized model file, the interface file, and the environment deployment configuration file, create a deployment package directory structure; Based on the deployment package directory structure, the environment preparation script is copied to the script directory, the standardized model file is copied to the model directory, the interface file is copied to the interface directory, and the environment deployment configuration file is copied to the configuration directory to generate the deployment package directory; Based on the deployment package directory, generate container build files; Based on the deployment package directory and the container build file, a compression and packaging operation is performed to obtain the deployment data package.

[0106] First, based on environment preparation scripts, standardized model files, interface files, and environment deployment configuration files, a standardized deployment package directory structure is created. The deployment package directory structure is an organized file system layout used to store all components required for deployment, and it follows specific organizational standards to ensure that deployment tools and systems can correctly identify and process each component. A well-designed directory structure is crucial for achieving automation, standardization, and maintainability in the deployment process.

[0107] Environment preparation scripts are a set of executable code files used to configure the deployment environment. They are primarily responsible for preparing the software environment, dependent libraries, and system configurations required for model operation. These scripts typically include several main types: dependency installation scripts (responsible for installing libraries, tools, and frameworks that the model depends on, such as specific versions of TensorFlow and PyTorch), environment variable configuration scripts (setting environment variables required for model operation, such as paths and memory limits), system configuration scripts (adjusting operating system or container settings, such as memory allocation and CPU affinity), and startup scripts (used to initialize services and start the model service process). Environment preparation scripts are designed according to the idempotency principle, meaning the scripts can be safely executed multiple times without side effects, ensuring the repeatability and robustness of the deployment process.

[0108] Standardized model files are machine learning model files that have been transformed, optimized, and validated, and can be directly loaded and used by the target deployment platform. The standardization process ensures that the model files have a unified format, clear structure, and compatibility with the target runtime environment. Standardized model files typically include core model files (such as TensorFlow's SavedModel, ONNX format .onnx files, or TensorRT's .engine files), model metadata (descriptions of model version, input / output specifications, performance characteristics, etc.), and preprocessing and post-processing logic (such as data transformation logic that needs to be embedded in the model). Standardization may involve steps such as model transformation (converting the original framework model to a format supported by the deployment framework), quantization (converting floating-point parameters to integers to improve performance and reduce memory usage), and optimization (applying techniques such as graph optimization and operation fusion to improve inference efficiency).

[0109] Interface files are the code and configuration files that define how the model interacts with external systems. They establish the API specifications and communication protocols for the model service. Interface files typically include API definition files (such as the Swagger specification for RESTful APIs, or the protobuf definition for gRPC), server code (such as Flask or FastAPI applications, or gRPC server implementations), client SDKs (programming interface libraries provided to callers), and request processing logic (code handling input validation, preprocessing, inference calls, and post-processing). Good interface design considers API version management, error handling, performance monitoring, and load management to ensure the model service has good availability, maintainability, and scalability.

[0110] Environment deployment configuration files are text files that describe the requirements and configurations of the deployment environment, guiding the deployment system on how to correctly set up and run model services. These files typically include resource configuration files (defining resource requirements and limits such as CPU, memory, and GPU), network configurations (port mapping, network policies, load balancing settings, etc.), scaling policies (defining when and how to scale service instances horizontally or vertically), and monitoring configurations (setting which metrics to collect, alert thresholds, etc.). Configuration files usually use structured formats such as YAML, JSON, or XML for easy parsing and processing by automation tools.

[0111] Creating a deployment package directory structure involves designing a logically clear and hierarchically organized file organization scheme. A standard deployment package directory structure typically includes the following main directories: 1. `scripts` directory: Contains all environment preparation and deployment scripts, which may be further divided into subdirectories such as installation scripts, configuration scripts, and startup scripts; 2. `model` directory: Contains standardized model files and related resources, such as weight files, graph definitions, and model metadata; 3. `interface` directory: Contains all interface definitions and implementations, such as API specifications, service code, and client libraries; 4. `config` directory: Contains configuration files, including environment configuration, service configuration, and resource specifications; 5. `docs` directory: Contains documentation and usage instructions to help deployment personnel and users understand service functions and deployment requirements; 6. `tests` directory: Contains test cases and verification scripts used to verify successful deployment. The directory structure design follows the separation of concerns principle, organizing components with different responsibilities separately for easier management and maintenance. Directory naming and organization typically follow industry best practices or internal organizational standards to ensure consistency and understandability.

[0112] During the directory structure creation process, necessary index files and manifests are generated, recording the included components, versions, and dependencies to facilitate verification and management by deployment tools and systems. This process also performs basic consistency checks to ensure that all necessary components are provided and that there are no significant compatibility issues.

[0113] After designing the deployment package directory structure, various files are copied to their respective directories based on this structure, forming a complete deployment package directory. This process uses file system operations to accurately place source files into target locations according to predefined mapping relationships. Specifically, environment preparation scripts are copied to the script directory (usually ` / scripts` or its subdirectories), standardized model files are copied to the model directory (usually ` / model`), interface files are copied to the interface directory (usually ` / interface`), and environment deployment configuration files are copied to the configuration directory (usually ` / config`).

[0114] During file copying, necessary permission settings and attribute maintenance are performed to ensure that executable files (such as scripts) have appropriate execution permissions and configuration files have appropriate read and write permissions. For executable scripts, Unix newline character conversion (when migrating between Windows and Unix systems) and interpreter path (shebang line) adjustments may also be performed to ensure that the script can execute correctly in the target environment. After file copying is complete, a deployment manifest file is generated, usually located in the root directory of the deployment package. This manifest file records the paths, checksums, version information, and dependencies of all files in the package. This manifest file helps the deployment system perform integrity verification, detect file corruption or missing files, and track the version history of the deployment package.

[0115] Next, based on the deployment package directory, container build files are generated. Container build files are sets of instructions that define how to transform the deployment package into a container image; the most common are Dockerfiles (for Docker containers) or similar specification files. Containerization is a technology that encapsulates an application and its dependencies in a self-contained, portable unit. It offers advantages such as environment consistency, ease of deployment, and resource isolation, and has become the mainstream method for deploying machine learning models. A Dockerfile is a text file containing a series of instructions that tell Docker how to build the image. A typical Dockerfile for deploying a machine learning model usually includes the following main parts: 1. Base image selection (FROM instruction): Select a suitable base image, such as an official image containing a specific version of Python and machine learning libraries, or a pre-built base image within the organization; 2. Metadata settings (LABEL instruction): Add metadata information for the image, such as maintainer, version, description, etc.; 3. Environment variable configuration (ENV instruction): Set the environment variables required for the runtime environment; 4. Dependency installation (RUN instruction): Execute commands to install additional dependencies required by the model; 5. File copying (COPY instruction): Copy files from the deployment package directory to the appropriate location within the container; 6. Port exposure (EXPOSE instruction): Declare the network ports that the container will use; 7. Startup command definition (CMD or ENTRYPOINT instruction): Specify the command to be executed when the container starts, usually a script to start the model service. Customization is performed based on the characteristics of the model and deployment requirements when generating the Dockerfile. For example, for GPU-accelerated models, a base image with CUDA support might be selected; for memory-intensive models, specific memory management parameters might be set; and for models that require special hardware or system libraries, corresponding installation and configuration steps might be added.

[0116] In addition to the standard Dockerfile, auxiliary build files may also be generated, such as the .dockerignore file (which specifies files to be ignored during the build to reduce the context size), the docker-compose.yml file (used to define the combined deployment of multi-container applications), or configuration files for specific container orchestration platforms (such as YAML configuration for Kubernetes).

[0117] The process of generating container build files integrates containerization best practices with the deployment requirements of specific models, aiming to create a container image that is minimal in size, securely hardened, and performance optimized. This includes using multi-stage builds to reduce image size, removing unnecessary development tools and debugging information, configuring appropriate user permissions (avoiding the use of the root user), and setting up health checks and graceful shutdown mechanisms.

[0118] Finally, based on the prepared deployment package directory and container build files, a compression and packaging operation is performed to obtain the final deployment package. The deployment package is a self-contained compressed file containing all the resources and instructions required for deployment, facilitating easy transfer and deployment between different environments. This step integrates all the previously prepared content into a single file, simplifying storage, distribution, and version management.

[0119] Compression and packaging operations typically involve the following steps: First, file selection and filtering: based on packaging rules, determine which files should be included in the deployment package, excluding unnecessary content such as temporary files, cached data, or development tools; second, compression algorithm selection: choose an appropriate algorithm based on content characteristics and compression requirements, such as using gzip for text files and choosing a lighter compression method for already compressed model files; third, metadata addition: add descriptive information such as creation time, version number, and compatibility requirements during the packaging process; and finally, integrity verification: calculate the checksum of the files within the package and add it to the package metadata to ensure that the integrity of the package can be verified later.

[0120] In some advanced deployment scenarios, deployment packages may also include digital signatures or encryption protection to ensure the authenticity and security of the package content. This typically uses public-key cryptography to sign or fully encrypt the package content, ensuring that only systems with the corresponding key can verify or decrypt the package content. After the deployment package is generated, a final verification is usually performed to ensure its integrity and validity. This may include checksum verification (confirming that files are not corrupted), structure verification (checking the existence of necessary files and directories), capacity checks (confirming that the package size is within the expected range), and simulated deployment testing (testing the deployment process in an isolated environment). Upon successful verification, metadata records for the deployment package are generated, including size, checksum, version identifier, compatibility information, etc., facilitating subsequent management and tracking.

[0121] The final deployment package is the core artifact of the model deployment process, encapsulating all necessary components and configurations to enable the model to run consistently and reliably across various target environments. Through a standardized directory structure, containerization support, and a complete packaging process, the deployment package decouples the development and production environments, greatly simplifying the model deployment and management process and improving deployment efficiency and success rate.

[0122] Deployment packages are typically uploaded to artifact repositories such as Docker Registry, Artifactory, or Amazon S3 for version management and distribution. During actual deployment, the deployment system pulls the specified version of the deployment package from the repository, decompresses it to the target environment or imports it into the container platform, and then completes the deployment process according to the instructions within the package. This standardized and automated deployment process significantly reduces human error and improves deployment consistency, which is a key guarantee for the successful operation of machine learning models in production environments.

[0123] A well-developed deployment package building process embodies DevOps and MLOps best practices, transforming complex model deployment requirements into standardized, repeatable operational steps. This supports rapid iteration and continuous deployment of machine learning models, ultimately achieving a seamless transition from model training to production services. Through this systematic approach, even the most complex machine learning models can be deployed to production environments in a controlled and efficient manner, providing continuous business value to organizations.

[0124] In some embodiments, the step of performing automated deployment based on the target environment credentials and the deployment data package to obtain deployment results, and performing operational quality assessment based on the deployment results and the parameter configuration scheme to obtain service quality assessment results, and optimizing the parameter configuration scheme and the deployment data package based on the service quality assessment results, includes: Based on the environment connection status and the deployment data packet, the deployment file is unpacked, and the corresponding environment API is called to perform the deployment operation to obtain the deployment result; Based on the deployment results and the parameter configuration scheme, the monitoring system data is configured, and the collection indicators and collection frequency are set. Based on the monitoring system data, the collection indicators and the collection frequency, the monitoring configuration data and initial state data are obtained. Based on the monitoring configuration data and the initial state data, model service operation data is collected, and periodic quality assessments are performed to obtain service quality assessment results. Based on the service quality assessment results and the model service operation data, performance anomalies and quality deviations are detected, corresponding alarm information and optimization suggestions are generated, and the parameter configuration scheme and the deployment data package are optimized according to the optimization suggestions and the optimized service status.

[0125] First, based on the environment connection status and deployment data package, the deployment files are unpacked. The unpacking process restores the compressed deployment data package to its original file structure, including verifying data package integrity (through checksum checks), decompressing files, restoring directory structure and permission settings, etc. After unpacking, a complete set of deployment files is obtained, including environment preparation scripts, standardized model files, interface files, and environment deployment configuration files. Next, the corresponding environment API is called to execute the deployment operation. The environment API is a programming interface provided by the target environment, allowing external systems to programmatically manage and manipulate environment resources. Different types of environments have different APIs, such as Kubernetes' kube-apiserver, cloud platform cloud management APIs, or local system shell command interfaces.

[0126] Deployment typically involves four main steps: First, the environment preparation script is executed, installing dependencies and configuring system parameters. Then, necessary resources, such as containers, services, and network policies, are created according to the environment deployment configuration file. Next, standardized model files are loaded and the model service is started. Finally, the API interface service is configured and started, enabling external systems to access the model's functionality. Each step has detailed execution logs and status checks to monitor the process and handle potential errors. Ultimately, the deployment result is obtained, which is the complete output information of the deployment operation, including a list of successfully deployed resources, service access points (such as URLs and ports), resource usage, and possible warnings or error messages. The deployment result serves as the basis for evaluating deployment success and is an important reference for subsequent monitoring and optimization.

[0127] Based on the deployment results and parameter configuration scheme, the monitoring system data is configured. Monitoring system data contains configuration information about how to monitor the model service, including monitoring targets (services and resources to be observed), monitoring methods (how to collect data), and monitoring parameters (collection frequency, thresholds, etc.). Monitoring targets are determined based on the deployment results, including deployed service instances, containers, and infrastructure resources. Simultaneously, key monitoring metrics and thresholds are determined based on performance expectations and resource allocation in the parameter configuration scheme. Regarding monitoring methods, approaches such as proxy collection (deploying a monitoring proxy within the service environment), API polling (periodically calling service and resource APIs to obtain status), or log analysis (processing log files generated by the service) may be used, depending on the monitoring capabilities of the target environment and the characteristics of the model service.

[0128] Setting the collection metrics and frequency is a crucial step in monitoring configuration. Collection metrics are specific data points that need to be measured and recorded, including system metrics (such as CPU utilization, memory usage, and network throughput), application metrics (such as request queue length, processing latency, and error rate), and business metrics (such as inference accuracy and model confidence). Different types of models focus on different metrics; for example, image recognition models may focus on inference time and GPU utilization, while recommendation systems may focus more on response latency and cache hit rate. The collection frequency determines the time interval for data collection, requiring a balance between monitoring accuracy and system overhead. High-frequency collection (e.g., per second) provides more granular observations and is suitable for scenarios with high real-time requirements; low-frequency collection (e.g., per minute or per hour) reduces overhead and is suitable for long-term trend analysis. The collection frequency can be automatically adjusted based on the importance and rate of change of the metrics; for example, CPU utilization might be collected every 5 seconds, while disk usage might be collected every hour.

[0129] Based on monitoring system data, collected metrics, and collection frequency, monitoring configuration data and initial state data are generated. Monitoring configuration data is a set of structured configurations that guide how the monitoring system operates, typically provided to monitoring tools (such as Prometheus, Grafana, and CloudWatch) in the form of configuration files or API calls. Initial state data is the baseline performance data of the model service immediately after deployment, serving as a reference point for subsequent evaluation. Complete state data, including key metrics such as resource usage, service response time, and error rate, is collected during the stable period after service startup (usually a few minutes or longer after startup) to establish a performance benchmark.

[0130] Next, based on monitoring configuration data and initial state data, the collection of model service runtime data begins. Model service runtime data is a time series of various performance and quality metrics generated during actual service operation, reflecting the service's health and performance. The data collection process is continuous and automated, periodically retrieving the latest data from the service and infrastructure according to the configured frequency and storing it in a time-series database or monitoring system to maintain a complete historical record. The collected data typically includes technical metrics (such as CPU / GPU utilization, memory consumption, network traffic, etc.) and business metrics (such as inference accuracy, request success rate, average response time, etc.). Based on the collected runtime data, periodic quality assessments are performed.

[0131] Periodic quality assessment is a comprehensive evaluation of the overall performance of a model service at pre-defined time intervals (e.g., hourly, daily, or weekly). The assessment process includes performance analysis (e.g., throughput, latency distribution, resource efficiency), stability assessment (e.g., error rate, crash frequency, service availability), and business metric analysis (e.g., prediction accuracy, user satisfaction). Current assessment results are compared with historical data, initial baselines, and predetermined goals to identify performance trends and potential problems. The result is a service quality assessment report, a structured report comprehensively reflecting the service quality status, including key indicator scores, performance trend charts, a problem list, and improvement opportunities.

[0132] Based on service quality assessment results and model service operation data, performance anomalies and quality deviations are detected. Performance anomalies refer to situations where service performance indicators deviate from the expected range, such as a sudden increase in response time, a continuous rise in memory usage, and request queue backlog. The system employs multiple anomaly detection algorithms to identify these situations, including statistical threshold detection (setting normal ranges based on the mean and standard deviation of indicators), time series analysis (detecting data trends and seasonal changes), and machine learning models (learning normal patterns and identifying abnormal patterns). Quality deviations refer to the gap between the model's output quality and the expected target, such as decreased classification accuracy and increased prediction bias. By comparing current quality indicators with historical baselines and combining domain-specific quality assessment methods, significant quality changes are identified.

[0133] Upon detecting anomalies and deviations, corresponding alarm messages and optimization suggestions are generated. Alarm messages are structured notifications about the detected problems, including a problem description, severity, scope of impact, discovery time, and relevant evidence, and are sent to relevant personnel through predetermined channels (such as email, messaging systems, or management consoles). Optimization suggestions are improvement plans proposed by the system based on problem analysis, such as "Increase the number of CPU cores to 8 to resolve the computational bottleneck," "Adjust the batch size to 32 to improve throughput," or "Enable model caching to reduce redundant calculations." These suggestions are generated based on the system's diagnosis of performance bottlenecks and analysis of resource utilization patterns, combined with preset optimization rules and historical optimization experience.

[0134] Based on the optimization suggestions and the optimized service status, optimize the parameter configuration scheme and deployment package. Optimizing the parameter configuration scheme involves adjusting resource allocation (e.g., increasing or decreasing CPU / GPU resources), modifying runtime parameters (e.g., batch size, concurrency), and updating feature configurations (e.g., enabling / disabling specific features). Optimizing the deployment package may include updating model files (e.g., using quantized versions to improve performance), modifying interface implementations (e.g., adding a caching layer), or adjusting environment configurations (e.g., optimizing network settings). The optimization process can be automated or manually assisted, depending on the level of autonomy and the complexity of the optimization. Once the optimization scheme is determined, a new configuration scheme and deployment package are generated, which may trigger a redeployment process, enabling continuous service optimization.

[0135] In this embodiment, this closed-loop mechanism of automated deployment and continuous optimization ensures that the machine learning model service can adapt to dynamically changing workloads and environmental conditions, maintaining optimal performance. Through systematic monitoring, evaluation, and optimization, the machine learning model transforms from a statically deployed service to a dynamically managed service, with its performance, stability, and resource efficiency continuously improved, maximizing the model's business value and return on investment. Simultaneously, the operational data and optimization experience accumulated in this process provide valuable references for future model deployments, forming a virtuous cycle of knowledge accumulation and capability enhancement.

[0136] In some embodiments, the step of extracting network structure information by calling a preset framework API based on the framework type and the parser instance includes: Based on the network structure information, the original feature information is extracted, which includes feature name, data type and dimension; Based on the original feature information, a corresponding mutual information calculation engine is selected based on the variable type. The variable type includes continuous variables, discrete-continuous variable pairs, and discrete variables. The mutual information calculation engine includes a preset k-nearest neighbor-based mutual information estimator selected for the continuous variable pairs, a preset histogram-based mutual information estimator selected for the discrete-continuous variable pairs, and a preset frequency-based mutual information estimator selected for the discrete variable pairs. Based on the mutual information calculation engine, the mutual information value between each original feature information and the target variable is calculated, and the feature importance ranking is generated based on the mutual information value to obtain the corresponding enhanced feature importance report; The network structure information is updated based on the enhanced feature importance report.

[0137] In this embodiment, network structure information is a detailed description of the model's internal architecture, including hierarchical organization (such as convolutional layers, fully connected layers, activation functions, etc.), computation graph topology (data flow between components), weight distribution (statistical characteristics of parameters), and hyperparameter configuration (such as kernel size, stride, etc.). This information reflects the model's design concept and processing logic, and forms the basis for subsequent analysis and optimization. Based on the extracted network structure information, raw feature information is further extracted. Raw feature information describes the data features received by the model's input layer, including three core attributes: feature name (a unique label identifying a specific feature, such as "image width," "user age," etc.), data type (the storage format of feature values, such as integers, floating-point numbers, strings, or booleans, etc.), and dimension (the shape and size of the feature, such as scalars, vectors, or multidimensional tensors, etc.). This information is crucial for understanding the model's input requirements and preprocessing needs. By analyzing the model's input layer definitions, variable declarations, and data flow graphs, all input features and their attributes are identified, forming a complete feature list.

[0138] After obtaining the raw feature information, it is necessary to assess the importance of each feature. To this end, an appropriate mutual information calculation engine is selected based on the variable type of the feature. Variable type is a classification of the statistical attributes of data features, mainly including three categories: continuous variables (numerical variables that can take any value within the real number range, such as height, temperature, etc.), discrete-continuous variable pairs (data pairs consisting of a discrete variable and a continuous variable, such as gender and income, education level and salary, etc.), and discrete variables (variables that can only take a finite number of different values, such as category, grade, etc.). The mutual information calculation engine is an algorithmic component specifically designed to calculate the degree of information dependence between different types of variables. Mutual information is a measure in information theory of the degree of interdependence between two variables, representing the degree to which knowing one variable reduces the uncertainty about another variable. The higher the mutual information value, the stronger the correlation or dependence between the two variables.

[0139] For different types of variables, appropriate mutual information estimators are selected. For continuous variable pairs (such as between two numerical features), a k-nearest neighbor-based mutual information estimator is used. This estimator is based on the k-Nearest Neighbors algorithm, which estimates the probability density by finding the nearest neighbors of data points in a multidimensional space, and then calculates the mutual information. The specific steps include: first, constructing an efficient spatial index structure (such as a KD-tree or Ball tree) to accelerate the nearest neighbor search; then, finding the k nearest neighbors for each data point; next, calculating the statistical properties of the nearest neighbor distances; and finally, estimating the mutual information value using these statistics. The advantage of the k-nearest neighbor method is that it does not assume the data distribution and can capture complex nonlinear relationships, but its computational complexity is high, requiring a balance between accuracy and efficiency.

[0140] For discrete-continuous variable pairs (such as categorical and numerical features), a histogram-based mutual information estimator is used. This estimator divides the continuous variable space into multiple intervals (i.e., constructs a histogram), and then calculates the joint and marginal frequencies of the discrete variable with these intervals to estimate the mutual information. The specific steps include: first, determining an appropriate binning strategy, such as equal-width binning, equal-frequency binning, or adaptive binning; then, mapping the continuous variable data to the corresponding bins; next, calculating the joint frequency of the discrete variable with the bins and their respective marginal frequencies; and finally, calculating the mutual information based on these frequencies. The histogram method is computationally efficient and simple to implement, but its accuracy depends on the choice of binning strategy; inappropriate binning may lead to information loss or bias.

[0141] For discrete variable pairs (such as between two categorical features), a frequency-based mutual information estimator is used. This estimator directly calculates mutual information by statistically analyzing the joint frequency and marginal frequencies of the variables. The specific steps include: first, constructing a contingency table of all possible value combinations; then, calculating the joint probability (frequency) of each combination and the marginal probabilities of each variable; finally, calculating the mutual information value based on these probabilities. While the frequency method is efficient and intuitive for discrete variables, it may face inaccurate estimation problems when dealing with high-dimensional sparse data or insufficient sample size.

[0142] Based on the selected mutual information calculation engine, the mutual information value between each original feature and the target variable is calculated. The target variable is the object that the model predicts or classifies, i.e., the label or dependent variable. For each feature, a mutual information estimator suitable for that feature type is used to calculate its mutual information value with the target variable, quantifying the feature's information contribution to the prediction target. Then, a feature importance ranking is generated based on the mutual information values, arranging features from high to low mutual information values ​​to form a clear hierarchy of feature influence. This ranking result, along with detailed attribute information of the original features, constitutes the Enhanced Feature Importance Report. The Enhanced Feature Importance Report is a structured document that includes not only the feature importance ranking but also rich information such as detailed attributes of each feature, mutual information values, data distribution characteristics, and potential correlation patterns. This report provides data scientists and engineers with a deep perspective on the importance of model input features, aiding in feature selection, model optimization, and business interpretation.

[0143] Finally, the network structure information is updated based on the enhanced feature importance report. The update process includes labeling important features, adjusting feature weight representations, optimizing the input layer structure, and adding feature importance metadata. The updated network structure information not only includes the model's architecture and parameters but also incorporates insights into feature importance, making the model's analytical results more comprehensive and valuable. This enhanced structure information provides a more solid foundation for subsequent model deployment and service-oriented architecture, particularly in performance optimization, resource allocation, and interpretable analysis.

[0144] In this embodiment, this series of steps achieves a transformation from basic network structure to in-depth feature importance analysis, greatly enhancing the depth and value of model analysis. Feature importance analysis not only helps in understanding the decision-making basis of the model but also provides a scientific basis for model simplification, feature engineering, and business interpretation, making it an indispensable part of modern machine learning engineering. The classification mutual information calculation method adopted ensures both the scientific rigor and accuracy of the analysis while taking into account the characteristics of different data types, reflecting a well-thought-out technical design and implementation strategy.

[0145] In some embodiments, updating the network structure information based on the enhanced feature importance report includes: Based on the enhanced feature importance report, a mutual information matrix between features is calculated, and based on the mutual information matrix, highly correlated feature groups are identified, so that a feature correlation matrix is ​​obtained from the highly correlated feature groups; Based on the feature correlation matrix, a feature dependency graph is constructed using a preset graph theory model, wherein each node in the feature dependency graph represents the original feature information, and the edge weights in the feature dependency graph represent the mutual information values. Community detection is performed on the feature dependency graph to identify feature subsets whose correlation is greater than a preset correlation threshold, and the centrality index of each node is calculated based on the feature subsets to obtain feature structure information. The network structure information is updated based on the structured feature information and the enhanced feature importance report.

[0146] In this embodiment, at a deeper stage of machine learning model analysis, it is necessary to further explore the complex relationships between features based on the enhanced feature importance report and integrate these relationships into the network structure information. The enhanced feature importance report is the result of previous analysis, containing mutual information values ​​between features and the target variable, feature importance ranking, and basic attribute information of the features. This report provides a foundation for understanding the importance of individual features, but further in-depth analysis is needed to fully grasp the interactions and structured relationships between features.

[0147] First, based on the enhanced feature importance report, the mutual information matrix between features is calculated. The mutual information matrix is ​​an N×N square matrix (where N is the number of features), and each element M(i,j) represents the mutual information value between the i-th feature and the j-th feature. The mutual information value quantifies the amount of information shared between two features, reflecting the degree of statistical dependence between them. During the calculation, all possible feature pairs are traversed, and an appropriate mutual information estimator is selected based on the variable type of each pair (continuous-continuous, discrete-continuous, or discrete-discrete) to calculate their mutual information values. For N features, N(N-1) / 2 mutual information values ​​need to be calculated (because mutual information is symmetric, and the mutual information between a feature and itself usually does not need to be calculated). This calculation process can be quite time-consuming, especially when the number of features is large or the data dimensionality is high. Therefore, a parallel computing strategy is usually adopted, distributing the task to multiple processing units for simultaneous execution.

[0148] After calculating the mutual information matrix, highly correlated feature groups are identified based on this matrix. Highly correlated feature groups refer to feature sets whose mutual information values ​​exceed a predetermined threshold. These features share a large amount of information and may represent similar or redundant information. The identification process typically employs clustering or threshold segmentation methods: the mutual information matrix can be treated as a similarity matrix, and algorithms such as hierarchical clustering, density clustering, or spectral clustering can be applied to group the features; alternatively, a mutual information threshold can be directly set (e.g., based on empirical values ​​or data distribution), and feature pairs with mutual information exceeding this threshold are considered highly correlated. The identified feature groups form a feature correlation matrix, a data structure representing the structured relationships between features. This can be a sparse matrix (retaining only highly correlated connections) or a labeled matrix (indicating group affiliation). The feature correlation matrix reveals the inherent structure in the feature space, helping to understand how the data is organized and potential redundancy.

[0149] Next, based on the feature relevance matrix, a feature dependency graph is constructed using a pre-defined graph theory model. A feature dependency graph is a network structure that represents the relationships between features in graph form, where nodes represent features and edges represent the strength of the association between features. The pre-defined graph theory model refers to the graph construction and analysis methods used, such as undirected weighted graphs, directed causal graphs, or minimum spanning trees; the choice of model depends on the analysis objectives and data characteristics. During the construction process, each original feature information is mapped to a node in the graph. Node attributes include basic information such as the feature name, data type, and dimension, as well as importance metrics extracted from the enhanced feature importance report. The edges between nodes are determined by the values ​​in the feature relevance matrix; only feature pairs with a relevance exceeding a certain threshold are connected. The weight of the edge is determined by the mutual information value; the higher the mutual information value, the greater the edge weight, indicating a stronger dependency between the two features.

[0150] The specific steps for constructing a feature dependency graph include: First, initializing the graph structure by creating nodes equal to the number of features and setting attributes for each node; then, traversing the feature correlation matrix, adding edges between corresponding nodes for each pair of features with a correlation exceeding a threshold, and setting the edge weights to mutual information values; finally, optimizing the graph structure, such as removing redundant edges and adjusting the layout to improve visualization. The completed feature dependency graph is a rich data structure that not only captures the binary relationships between features but also reflects the complex interaction patterns between multiple features through network topology. This graph representation provides a foundation for subsequent community detection and centrality analysis.

[0151] After constructing the feature dependency graph, community detection is performed to identify feature subsets with a correlation greater than a preset correlation threshold. Community detection is an algorithm in graph theory that aims to discover tightly connected groups of nodes in a graph, where the connections within these groups are dense, while connections to other groups are relatively sparse. In feature analysis, communities represent highly correlated feature groups that may collectively describe a certain aspect or dimension of the data. Various community detection algorithms are employed, such as the Louvain algorithm, label propagation algorithm, and Girvan-Newman algorithm. The Louvain algorithm is a modularity optimization-based method that iteratively merges nodes to maximize the modularity score; the label propagation algorithm simulates the propagation of information in the network, updating the label of the current node based on the labels of its neighbors; the Girvan-Newman algorithm segments the network by progressively removing edges with high betweenness centrality. Appropriate algorithms are selected based on the graph's size and characteristics, and a correlation threshold is set to control the granularity and density of communities.

[0152] The preset association threshold is the standard by which the system determines whether nodes belong to the same community. It can be set based on domain knowledge, data characteristics, or analysis objectives. A higher threshold requires denser connections within the community, resulting in more refined community identification; a lower threshold allows for looser connections, leading to coarser community divisions. The result of community detection is a series of feature subsets, each containing closely related features. These subsets may correspond to different dimensions or aspects of the data. For example, a user behavior model might have feature subsets describing basic attributes, activity levels, and consumption habits.

[0153] Based on the identified feature subsets, the centrality metric for each node is calculated. Centrality metrics are measures of node importance in network analysis, reflecting a node's position and influence within the network. Commonly used centrality metrics include degree centrality, betweenness centrality, proximity centrality, and eigenvector centrality. Degree centrality is the simplest metric, calculating the number of edges a node connects to, reflecting its direct connectivity. Betweenness centrality measures the frequency with which a node acts as a "bridge" between other nodes in the network, representing its control. Proximity centrality measures the reciprocal of the average distance from a node to all other nodes in the network, reflecting the node's closeness to the entire network. Eigenvector centrality considers the importance of other nodes a node connects to and is a recursively defined centrality metric. One or more centrality metrics are calculated according to the analysis requirements, and these metrics are combined with other attributes of the nodes (features) to form structured feature information.

[0154] Structured feature information is a rich representation of features that includes not only basic attributes (such as name, type, and dimension) but also their position (community) and importance (centrality index) within the feature network. This representation captures the multidimensional nature of features, considering both the relationship between features and the target variable (through mutual information values) and the interactions between features (through network structure and community partitioning), providing a comprehensive reference for feature selection and model interpretation. Structured feature information is typically stored in structured data formats such as JSON or XML, facilitating system processing and manual analysis.

[0155] Finally, the network structure information is updated based on the structured feature information and enhanced feature importance reports. During the update process, newly discovered feature relationships and structures are integrated into the network representation, such as labeling feature community affiliations, recording feature centrality indices, and adjusting the input layer's organizational structure to reflect feature correlations. The updated network structure information not only includes the model's hierarchical organization and computational flow but also incorporates the complex relationships between features, providing a more comprehensive perspective for model analysis and optimization. For example, it may automatically generate feature combination suggestions based on feature communities or optimize the feature processing flow based on feature centrality, improving the model's efficiency and interpretability.

[0156] In this embodiment, this graph theory-based feature relationship analysis method extends traditional single-feature importance evaluation to multi-feature interaction networks, revealing the deep structure and patterns of data and providing strong support for the understanding, optimization, and application of machine learning models. By identifying feature communities and computational centrality, it is possible to gain insights into the potential dimensions and key nodes in the data. These insights can guide feature selection, model simplification, and performance optimization, improving the efficiency and effectiveness of the model. Simultaneously, the structured representation of features enhances the interpretability of the model, enabling developers and users to more clearly understand the model's decision-making basis and reasoning process, which is particularly important in many fields requiring transparency, such as healthcare, finance, and law.

[0157] In some embodiments, the step of analyzing the matching degree between the model's computational characteristics and the environmental resource characteristics using a preset parameter optimization model based on the model analysis report and the deployment environment information, and predicting configuration parameters, and obtaining a parameter recommendation scheme based on the model's computational characteristics, the matching degree, and the predicted configuration parameters, includes: Based on the historical deployment data obtained in advance, a parameter performance mapping matrix is ​​constructed, wherein each row of the parameter performance mapping matrix represents a set of historical deployment data, and each column of the parameter performance mapping matrix represents a performance index. Based on the parameter performance mapping matrix, a corresponding penalty form is selected according to the type parameter to determine the hybrid non-convex penalty function. The type parameter includes discrete resource allocation parameters, continuous optimization parameters, and parameters that need to be grouped. The parameters that need to be grouped include memory parameters and concurrency parameters. The penalty form includes truncated L1 penalty corresponding to the discrete resource allocation parameters, smooth shearing absolute deviation penalty corresponding to the continuous optimization parameters, and grouping MCP penalty corresponding to the parameters that need to be grouped. Based on the hybrid nonconvex penalty function, an objective function and a corresponding optimization objective are constructed. The optimization objective is used to indicate minimizing the weighted sum of the performance prediction error of the objective function and the nonconvex penalty term. Based on the optimization objective, the objective function is solved using a preset coordinate descent model or a preset proximal gradient model to obtain a sparse parameter weight matrix. Based on the model analysis report, the model computation characteristics are extracted, and based on the model computation characteristics, the model computation requirements information is obtained. The model computation characteristics include the number of floating-point operations, the number of parameters, and the memory requirements. Based on the deployment environment information, environmental resource characteristics are extracted, and environmental resource information is obtained based on the environmental resource characteristics. Based on the model calculation demand information and the environmental resource information, the parameter optimization model is used to generate the matching degree between the model calculation characteristics and the environmental resource characteristics, and a matching degree score corresponding to the matching degree is obtained. Based on the matching score and the sparse parameter weight matrix, the parameter optimization model is used to predict static deployment parameters and dynamic operation parameters to obtain the parameter recommendation scheme.

[0158] First, a parameter performance mapping matrix is ​​constructed based on pre-acquired historical deployment data. Historical deployment data represents valuable experience collected from past model deployment practices, containing the performance of different models under various environments and parameter configurations. This data typically includes model characteristics (e.g., type, scale), environmental characteristics (e.g., CPU / GPU specifications), parameter configurations (e.g., batch size, number of threads), and performance metrics (e.g., inference latency, throughput, resource utilization). The parameter performance mapping matrix is ​​a structured data representation where each row represents a set of historical deployment instances, containing all configuration parameters and corresponding performance metrics for that instance; each column represents a specific parameter or performance metric. The matrix construction process includes data collection, cleaning, standardization, and organization. In the data collection phase, deployment records are obtained from logs, monitoring systems, and user feedback; the data cleaning phase removes outliers, handles missing data, and standardizes the format; the standardization phase adjusts data of different scales and units to a comparable range; finally, in the organization phase, the processed data is arranged into a matrix format by rows (deployment instances) and columns (parameters / metrics) to prepare for subsequent analysis.

[0159] After constructing the parameter performance mapping matrix, it is necessary to select the appropriate penalty form based on different parameter types to determine the hybrid non-convex penalty function. Distinguishing parameter types is crucial for using the most suitable modeling method for parameters with different characteristics. Discrete resource allocation parameters refer to resource configuration items that can only take discrete values, such as the number of CPU cores and GPU devices. These parameters typically have clear upper limits and increment steps. Continuously optimized parameters are those that can take arbitrary values ​​within a certain range, such as the learning rate and timeout threshold. These parameters usually require fine-tuning to achieve optimal performance. Parameters that need to be grouped refer to those parameters that are functionally or impactfully interrelated and should be considered as a whole. These mainly include memory parameters (such as cache size and memory pool size) and concurrency parameters (such as the number of threads and concurrent requests). These parameters have interactive effects, and optimizing them individually may lead to suboptimal solutions.

[0160] Different penalty forms are employed to construct hybrid nonconvex penalty functions for these different types of parameters. For discrete resource allocation parameters, the system uses truncated L1 penalty. Truncated L1 penalty is an improvement on standard L1 penalty; by introducing a truncation mechanism, the penalty stops increasing after a parameter value exceeds a certain threshold. This helps retain truly important parameters while compressing minor parameters to zero. This mechanism is particularly suitable for resource allocation because it encourages the system to concentrate resources on a few key configurations rather than dispersing them. For continuously optimized parameters, a smooth clipped absolute deviation (SCAD) penalty is used. SCAD is a commonly used nonconvex penalty function in statistical learning. Compared to L1 penalty, it is smoother when dealing with large coefficients, reduces bias, and is suitable for fine-tuning continuous parameters. For parameters requiring grouping, a grouped MCP (Minimax Concave Penalty) penalty is used. MCP is another non-convex penalty function that can reduce bias in the parameter selection process. Grouped MCP extends this idea by considering parameters in groups, ensuring that related parameters are selected or eliminated as a whole. This is especially important for handling parameter groups that affect each other, such as memory parameters and concurrency parameters.

[0161] By combining these penalty functions targeting different parameter types, a hybrid nonconvex penalty function is formed. This function can adapt to the characteristics of different parameters, providing a theoretical basis for subsequent optimization. The design of the hybrid nonconvex penalty function reflects a deep understanding of the optimization of model deployment parameters, taking into account both the discrete / continuous properties of the parameters and the interrelationships between them, thereby constructing a more realistic mathematical model in the parameter space.

[0162] Based on a hybrid nonconvex penalty function, an objective function and its corresponding optimization objective are constructed. The objective function is a mathematical expression used to evaluate the merits of a specific parameter configuration. Here, the objective function consists of two parts: a performance prediction error term and a nonconvex penalty term. The performance prediction error term measures the difference between the model's predicted performance and the actual performance under given parameters, typically calculated using mean squared error or mean absolute error. The nonconvex penalty term, through the aforementioned hybrid nonconvex penalty function, imposes constraints on different types of parameters. These two terms are weighted and combined using weighting coefficients to form the final objective function. The optimization objective is a clear mathematical expression: minimizing the value of the objective function. By minimizing this function, both the accuracy of performance prediction and the effective selection and grouping of parameters can be achieved, thus realizing the goal of model deployment optimization.

[0163] To solve the constructed objective function, two optimization algorithms can be used: coordinate descent or proximal gradient. Coordinate descent is an iterative optimization algorithm that optimizes only one coordinate direction (i.e., one parameter) in each iteration, while keeping the values ​​in other coordinate directions unchanged until convergence. This method is computationally simple and particularly suitable for handling large-scale sparse problems, but may converge slowly when there is strong correlation between parameters. Proximal gradient is a gradient descent method that utilizes proximal mappings. It can effectively handle non-smooth penalty terms and is particularly suitable for optimizing objective functions containing non-convex penalties. The appropriate algorithm should be selected based on the problem size, parameter characteristics, and computational resources. Regardless of the algorithm used, the ultimate goal is to obtain a sparse parameter weight matrix, which is a matrix indicating the importance of each parameter. Many elements are zero or close to zero, indicating that the corresponding parameter has little impact on performance and can be ignored or use default values; while elements with larger values ​​indicate that the corresponding parameter has a significant impact on performance and requires careful tuning. The sparsity of the sparse parameter weight matrix (i.e., the proportion of zero elements) reflects the degree of screening for parameter importance; high sparsity means that the system has identified a small number of key parameters.

[0164] Next, we extract the model computational characteristics based on the model analysis report. These characteristics provide a quantitative description of the model's computational requirements, primarily including three core metrics: floating-point operations (FLOPS), parameter count, and memory requirements. FLOPS measures the model's computational complexity, representing the number of floating-point operations (e.g., multiplication and addition) required for a single inference operation, directly impacting computation speed. Parameter count refers to the total number of parameters such as weights and biases that need to be stored in the model, directly affecting the model's storage requirements and transmission overhead. Memory requirements include the memory occupied by model parameters, intermediate computation results, and runtime buffers, influencing the system's memory resource needs. These characteristics collectively constitute the model computational requirement information, comprehensively describing the model's demands on computational resources. By analyzing the computational graph structure, operator characteristics, and data flow patterns in the model analysis report, we accurately calculate these metrics, forming a comprehensive understanding of the model's computational requirements.

[0165] Simultaneously, environmental resource characteristics are extracted based on deployment environment information. These characteristics describe the computing power and resource limitations provided by the target deployment environment, including processor type (CPU, GPU, TPU, etc.), number of processing cores, clock frequency, memory size, bandwidth, and storage capacity. These characteristics are integrated into environmental resource information, providing a foundation for subsequent matching analysis. The extraction of environmental resource information is typically based on system configuration files, hardware specification tables, or direct environment probing. In cloud or virtualized environments, the impact of resource sharing and scheduling strategies on actual available resources may also need to be considered.

[0166] Based on the extracted model computational requirements and environmental resource information, the model is optimized using parameters to generate a matching degree between the model's computational characteristics and the environmental resource characteristics, and a matching degree score is calculated. The matching degree is a multi-dimensional assessment that measures the ability of a specific environment to meet the model's requirements. The matching degree assessment considers several aspects: computational capacity matching (whether the environment can provide sufficient computational resources to handle the model's FLOPS requirements), memory matching (whether the environment has enough memory to accommodate model parameters and runtime data), and bandwidth matching (whether the data transfer rate meets the model's needs), etc. These matching indicators are combined to form a matching degree score, typically using a weighted average or a more complex scoring mechanism, reflecting the overall compatibility between the environment and the model. A high matching degree score indicates that the environment can efficiently support model operation, while a low score implies potential performance bottlenecks or insufficient resources.

[0167] Finally, based on the matching score and sparse parameter weight matrix, a parameter optimization model is used to predict static deployment parameters and dynamic runtime parameters, generating parameter recommendation schemes. Static deployment parameters are fixed configurations determined during model deployment, such as resource allocation (number of processor cores, memory limits, etc.), model optimization options (quantization level, graph optimization, etc.), and system configurations (cache size, thread pool, etc.). Dynamic runtime parameters are configurations that may be adjusted during model runtime, such as batch size, timeout settings, and concurrent request counts. The parameter optimization model comprehensively considers model characteristics, environmental resources, matching score, and parameter weights to recommend optimal values ​​for each important parameter. The recommendation process typically employs a strategy combining rule-based methods and machine learning methods: explicit rules are used for clearly understood parameter relationships; for complex parameter interactions, models learned from historical data are used for prediction. The generated parameter recommendation scheme is a structured document that details the name, value, impact, and confidence level (reliability assessment of the recommendation) of each recommended parameter, providing a comprehensive reference for model deployment.

[0168] This parameter optimization process demonstrates the system's deep understanding of machine learning model deployment. By integrating historical experience, mathematical optimization, and domain knowledge, it achieves intelligent recommendation of deployment parameters, contributing to improved model deployment efficiency and performance. The application of a non-convex penalty function enables the system to effectively handle the characteristics of different types of parameters, while matching degree analysis ensures that the recommended parameters are suitable for specific model-environment combinations. This customized recommendation scheme significantly reduces the workload of manual tuning and shortens the model's development-to-production cycle. Furthermore, the generated parameter weight matrix and matching degree score provide interpretable evidence for deployment decisions, enabling developers to understand the logic behind the recommendations and make manual interventions and adjustments when necessary.

[0169] In some embodiments, the step of predicting static deployment parameters and dynamic operating parameters using the parameter optimization model based on the matching score and the sparse parameter weight matrix to obtain the parameter recommendation scheme includes: Based on the sparse parameter weight matrix, the parameter value range is determined according to environmental constraints and model characteristics, and a parameter search space is constructed based on the parameter value range; Based on the sparse parameter weight matrix and the parameter search space, the pre-screening only considers combinations of non-zero weight parameters to obtain a simplified parameter space. Based on the simplified parameter space, a multi-objective optimization model is constructed, which is used to indicate the simultaneous minimization of inference latency, the maximization of throughput, the maximization of resource utilization efficiency, and the maintenance of specified model accuracy. Based on the multi-objective optimization model, the Pareto optimal solution set is searched to obtain multiple sets of optional parameter configuration data; Based on the multiple sets of optional parameter configuration data and preset user performance preference data, the parameter recommendation scheme is generated.

[0170] First, based on the sparse parameter weight matrix, and combined with environmental constraints and model characteristics, the parameter value range is determined, and a parameter search space is constructed. The sparse parameter weight matrix is ​​a key data structure generated during the initial optimization process. Each element in the matrix corresponds to the importance weight of a configuration parameter; non-zero elements represent parameters that have a significant impact on performance, while zero or near-zero elements represent parameters that can be ignored. Environmental constraints refer to the resource limitations and technical conditions of the deployment environment, including maximum available memory, processor capabilities, and bandwidth limitations. Model characteristics describe the computational requirements and structural features of the model, such as model size, computational complexity, and dependencies. The parameter value range defines the possible value intervals for each important parameter. These intervals are constrained by various factors: physical limitations (e.g., memory cannot be negative), technical limitations (e.g., the number of threads cannot exceed the number of processor cores), empirical limitations (e.g., batch size is usually most effective within a specific range), and model-specific limitations (e.g., some models may have special requirements for certain parameters).

[0171] Determining the range of parameter values ​​typically involves the following steps: First, an initial range is set based on the parameter type, such as the basic value space for integer, floating-point, or categorical parameters. Then, adjustments are made based on environmental constraints to ensure the parameter range does not exceed the resources available to the environment. Next, the range is further refined considering model characteristics to better suit the specific model's needs. Finally, fine-tuning may be performed based on historical experience data to concentrate the range within regions with historically good performance. The parameter search space is the Cartesian product of all parameter value ranges, representing all possible combinations of parameter configurations. In high-dimensional parameter spaces, this number of combinations can be enormous, making a direct search of the entire space impractical and requiring further simplification and optimization.

[0172] Next, based on the sparse parameter weight matrix and the constructed parameter search space, a pre-screening process considers only combinations of parameters with non-zero weights, resulting in a simplified parameter space. This pre-screening process leverages key information from the sparse parameter weight matrix, focusing attention on truly important parameters while ignoring those with negligible impact on performance. Specifically, a weight threshold is set, retaining only parameters with weights greater than this threshold as optimization targets, while other parameters are fixed to default values ​​or set empirically. This step significantly reduces the number of parameters to consider, thereby greatly shrinking the search space. For example, if the original parameter space contains 20 parameters, but the sparse weight matrix shows only 5 parameters with significantly non-zero weights, the search can be focused on these 5 key parameters, reducing the complexity of the search space from exponential to a manageable level.

[0173] Simplifying the construction of the parameter space may also involve handling parameter dependencies. Strong correlations or constraints exist between certain parameters, and these relationships can be used to further simplify the search. For example, if parameters A and B exhibit a clear linear relationship, it can be simplified to a single parameter; or if the value of parameter C is constrained by parameter D, this constraint can be directly encoded into the search space, avoiding consideration of invalid combinations. Through these techniques, a significantly simplified parameter space can be constructed that still captures key performance influencing factors, laying the foundation for subsequent optimization.

[0174] Based on a simplified parameter space, a multi-objective optimization model is constructed, considering multiple performance objectives simultaneously. Multi-objective optimization refers to the process of finding the optimal solution under multiple (often conflicting) objective functions. Common optimization objectives in machine learning model deployment include inference latency, throughput, resource utilization efficiency, and model accuracy. Inference latency refers to the time required from input data to obtaining the output result, usually measured in milliseconds or microseconds, directly impacting user experience, especially important in real-time applications. Throughput measures the number of requests or samples processed per unit of time, usually expressed as requests per second (RPS) or samples per second, reflecting processing capacity and crucial in high-load scenarios. Resource utilization efficiency assesses how effectively available resources (CPU, memory, bandwidth, etc.) are utilized, usually expressed as resource utilization rate or the ratio of performance to resource consumption, particularly important for cost-sensitive deployments. Model accuracy measures the accuracy of the model's output, which may be affected by certain optimization techniques (such as quantization and pruning), and has minimum requirements in many applications.

[0175] These objectives often involve trade-offs: for example, reducing latency may require allocating more resources, thus reducing resource utilization efficiency; increasing throughput may lead to increased latency; some optimizations may improve performance but sacrifice model accuracy. Constructing a multi-objective optimization model requires formalizing these objectives as mathematical functions and defining their relative importance. Common methods include weighted summation (combining multiple objective functions into a single function with weights), hierarchical optimization (optimizing objectives sequentially according to priority), or direct search of the Pareto optimal solution set (finding all non-dominated solutions without pre-setting weights). The appropriate method is usually chosen based on the characteristics of the problem and user requirements.

[0176] When constructing a multi-objective optimization model, the following steps are required: First, define clear evaluation metrics and calculation methods for each performance objective; then, determine the relative importance or priority of each objective, which may be based on user-specified preferences or application scenario requirements; next, establish a mapping relationship between parameter configurations and the performance of each objective, which may be based on performance models, historical data analysis, or online evaluation; finally, integrate the above elements into a formalized optimization problem, which can be a single weighted objective function or a Pareto optimization framework.

[0177] Based on the constructed multi-objective optimization model, the search for Pareto optimal solutions begins, yielding multiple sets of optional parameter configurations. The Pareto optimal solution set is a core concept in multi-objective optimization, referring to the set of all non-dominated solutions. A non-dominated solution (also called a Pareto optimal solution) is a solution that, by improving any one objective, necessarily leads to a worsening of at least one other objective. In other words, no solution in the Pareto optimal solution set can be completely surpassed by any other solution. These solutions form the so-called Pareto frontier, representing the optimal trade-off between the objectives.

[0178] There are various methods for searching for Pareto optimal solutions, commonly including: multi-objective evolutionary algorithms (such as NSGA-II, SPEA2, etc.), which simulate natural evolution, maintain a population of solutions, and gradually approach the Pareto front through selection, crossover, and mutation operations; multi-objective particle swarm optimization, where particles move in the search space, considering both global and individual optima, suitable for continuous parameter spaces; multi-objective simulated annealing, which allows the algorithm to occasionally accept suboptimal solutions to escape local optima; and multi-objective Bayesian optimization, particularly suitable for evaluating computationally expensive objective functions. The appropriate algorithm should be selected based on the problem size, computational resources, and time constraints, or multiple algorithms can be combined to improve search efficiency.

[0179] The search process is typically iterative: starting with an initial solution (which may be a randomly selected parameter configuration or an experience-based default configuration), new candidate solutions are generated and evaluated according to the strategy of the chosen algorithm, and the current Pareto front is updated until termination conditions are met (such as reaching the maximum number of iterations, the quality of the solution reaching a threshold, or the front stabilizing and no longer improving). To improve efficiency, parallel computation may be used to evaluate multiple candidate solutions simultaneously, or a surrogate model may be used to approximate the expensive objective function evaluation. After the search, a set of Pareto optimal parameter configurations is obtained, each representing the best choice under different objective weights.

[0180] Finally, based on multiple sets of optional parameter configuration data and preset user performance preference data, a parameter recommendation scheme is generated. User performance preference data refers to the degree of importance users attach to different performance indicators. For example, some users may pay more attention to low latency, while other users may focus more on high throughput or resource efficiency. These preferences can be explicitly specified (users directly provide the importance weights of each indicator) or implicitly inferred (based on the user's application scenario or historical choices).

[0181] The process of generating parameter recommendation schemes typically includes the following steps: First, calculate a comprehensive score for each solution on the Pareto front based on user preferences; this may be based on weighted summation or a more complex decision model. Then, select the solutions with the highest comprehensive scores as candidate recommendations. Next, final adjustments and validations may be performed to ensure the recommended parameters are feasible in a real-world environment. Finally, organize the recommended parameters and their expected performance metrics into a structured recommendation scheme. The recommendation scheme typically includes a primary recommendation (the parameter configuration with the highest score) and alternative recommendations (configurations with similar scores but different performance characteristics), along with detailed parameter values, expected performance metrics, and applicable scenario descriptions for each recommendation.

[0182] In addition, it provides confidence assessments and sensitivity analyses of the recommendations to help users understand their reliability and the potential impact of parameter fine-tuning. For example, it can indicate that some parameters have a significant impact on performance and require precise settings, while other parameters have a smaller impact and can be adjusted more freely. This additional information helps users make necessary adjustments to the recommendations based on their actual needs and conditions.

[0183] The entire parameter recommendation process reflects the system's comprehensive consideration of optimizing machine learning model deployment. By combining sparse parameter weight analysis, multi-objective optimization, and user preference considerations, it efficiently finds the most suitable configuration for a specific environment and needs within a vast parameter space. This approach not only reduces the workload of manual parameter tuning but also improves the quality of tuning, helping users make informed decisions in complex performance trade-offs. Simultaneously, the multiple recommendations and detailed performance expectations provided enhance the controllability and predictability of the deployment process, enabling users to select the most appropriate configuration based on their actual needs.

[0184] In some embodiments, configuring monitoring system data based on the deployment results and the parameter configuration scheme, and setting collection indicators and collection frequency, and obtaining monitoring configuration data and initial state data based on the monitoring system data, the collection indicators, and the collection frequency, includes: Based on the monitoring system data, the collection indicators, and the collection frequency, multidimensional indicators are obtained, including computing performance indicators, resource utilization indicators, quality indicators, and business indicators. Based on the multidimensional indicators, a hierarchical acquisition strategy is determined, which includes a high-frequency acquisition key delay indicator acquisition strategy, a mid-frequency acquisition resource utilization indicator acquisition strategy, and a low-frequency acquisition quality evaluation indicator acquisition strategy. Based on the hierarchical acquisition strategy, time series data on the multidimensional indicators are collected, and feature engineering processing is performed on the time series data to obtain a monitoring dataset. Based on the monitoring dataset, a multi-component Gaussian mixture model is trained using a preset expectation-maximization algorithm, and the optimal number of components is determined. For each component, the mean vector, covariance matrix, and mixture weights are learned, and the typical working state represented by each component is identified, thus forming a trained multi-component Gaussian mixture model. Based on the trained multi-component Gaussian mixture model, a mapping relationship from components to system states is established, and normal state boundaries are defined. Based on the mapping relationship and the normal state boundaries, monitoring configuration data and initial state data are obtained.

[0185] First, based on monitoring system data, collected metrics, and collection frequency, a multi-dimensional indicator system is determined. Multi-dimensional indicators are a comprehensive set of metrics describing the model's operational status, reflecting its performance, resource consumption, and business value from different dimensions. These indicators can be divided into four main categories: computational performance indicators, resource utilization indicators, quality indicators, and business indicators. Computational performance indicators primarily measure the model's computational efficiency and response speed, including inference latency (the time required for the model to process a single request, typically measured in milliseconds), throughput (the number of requests processed per unit of time, such as queries per second (QPS), batch processing efficiency (the percentage improvement in efficiency for batch processing requests), and computational density (the number of inference tasks completed per unit of computing resources). These indicators directly reflect the model's computational performance and are particularly important for applications with high real-time requirements.

[0186] Resource utilization metrics focus on the model's consumption of system resources, including CPU utilization (processor usage percentage), GPU utilization (graphics processor usage percentage), memory usage (the amount of memory required for model operation), peak memory usage (maximum memory usage during operation), bandwidth usage (data transfer rate), and energy consumption metrics (power consumption monitoring data). These metrics help understand the model's resource demand patterns and are crucial for resource planning and cost control. Especially in shared computing environments or resource-constrained scenarios, monitoring resource usage is essential to avoid resource contention and system bottlenecks.

[0187] Quality metrics focus on the accuracy and reliability of model output, including accuracy (the proportion of correctly predicted cases), precision (the proportion of true positives in positive predictions), recall (the proportion of correctly identified true positives), F1 score (the harmonic mean of precision and recall), AUC (area under the ROC curve), and confidence distribution (the deterministic distribution of model predictions). These metrics help monitor whether the model's performance in a production environment meets expectations and whether performance degradation or drift occurs. Continuous monitoring of quality metrics after model deployment is crucial for timely detection of model drift.

[0188] Business metrics directly relate to the actual value the model brings to the business, including conversion rate (the percentage of users who take the expected action), click-through rate (the frequency with which ads or recommendations are clicked), user satisfaction (satisfaction levels based on user feedback or surveys), revenue (revenue directly or indirectly contributed by the model), and return on investment (ROI) (the ratio of input costs to output returns). These metrics link technical performance to business objectives, helping to evaluate the commercial value and effectiveness of model deployment. Business metrics typically need to be integrated with business systems and may involve complex attribution analysis to accurately assess the model's contribution. These multidimensional metrics together constitute a comprehensive monitoring system, evaluating the model's performance from both technical and business perspectives. The selection and definition of metrics need to consider specific application scenarios, business objectives, and technical limitations to ensure that the collected data is both practically valuable and does not incur excessive monitoring overhead.

[0189] After identifying the multidimensional indicators, a tiered data collection strategy is developed based on these indicators. This tiered strategy is a resource-efficient method for collecting monitoring data, dividing collection tasks into different frequency levels according to the importance, frequency of change, and resource consumption of the indicators. This strategy effectively balances comprehensive monitoring with system overhead, ensuring timely monitoring of key indicators while avoiding unnecessary resource waste. The tiered data collection strategy mainly includes three levels: a high-frequency strategy for collecting key latency indicators, a mid-frequency strategy for collecting resource utilization indicators, and a low-frequency strategy for collecting quality assessment indicators.

[0190] High-frequency data collection strategies target performance metrics with extremely high real-time requirements, such as inference latency, request queue length, and system response time. These metrics directly impact user experience and require near real-time monitoring for rapid response to performance fluctuations. High-frequency data collection is typically measured in seconds or minutes, employing lightweight collection methods to minimize the impact of monitoring itself on the system. Implementation may involve using sliding window techniques to calculate moving averages and quantiles, or using subsampling and summary statistics methods to reduce data volume while retaining key statistical characteristics. High-frequency data is usually retained for a short period (e.g., a few hours or a day) and then downsampled to medium-frequency data for long-term storage.

[0191] Mid-frequency resource utilization metrics collection strategies focus on system resource usage, such as CPU / GPU utilization, memory usage, disk I / O, and network traffic. These metrics change relatively slowly but are crucial for understanding system load and resource bottlenecks. Mid-frequency collection is typically measured in minutes or hours, balancing monitoring granularity with storage costs. Mid-frequency data is usually retained for several days to weeks, providing a basis for mid-term trend analysis and pattern recognition. In terms of collection methods, periodic snapshots and threshold-triggered collection may be combined; when resource usage approaches a predetermined threshold, the sampling frequency is increased to improve sensitivity to potential problems.

[0192] Low-frequency data collection strategies focus on metrics related to model quality and business value, such as accuracy, conversion rate, and user satisfaction. These metrics typically require a sufficient sample size or business cycle to obtain a meaningful assessment, and computational costs are high. Low-frequency data collection may occur over hours, days, or longer periods, depending on business characteristics and data volume. Low-frequency data is usually stored long-term for model quality trend analysis and periodic pattern detection. The collection process may involve complex assessment logic, such as sampling evaluation, A / B testing comparisons, or manual review, to ensure the reliability and representativeness of the quality assessment.

[0193] This tiered data collection strategy allocates collection resources based on the characteristics and importance of metrics, ensuring that the most valuable monitoring data is obtained with limited resources. Strategy configuration is typically dynamic, automatically adjusting the collection frequency and depth based on system load, anomaly detection results, or critical business periods, improving monitoring efficiency and flexibility.

[0194] Based on a tiered data collection strategy, time-series data on multi-dimensional indicators was collected, and feature engineering was performed on this data to generate a monitoring dataset. Time-series data consists of continuous data points recorded in chronological order, reflecting the trends of indicators over time. For machine learning model monitoring, time-series data includes the evolution of model performance, resource usage, and business metrics over time, forming the basis for understanding model operating patterns and detecting anomalies. During the time-series data collection process, it is crucial to ensure accurate timestamps, data integrity, and consistent sampling, which are essential for subsequent analysis.

[0195] Feature engineering transforms raw time series data into a more suitable form for analysis and modeling. It involves several key steps: data cleaning (handling missing values, outliers, and noise), data standardization (converting indicators of different dimensions to the same scale), extraction of temporal features (such as periodicity, trend, and seasonal component decomposition), calculation of statistical features (such as moving averages, variance, quantiles, and kurtosis), and dimensionality reduction and feature selection (reducing data redundancy and dimensionality). The goal of feature engineering is to extract key patterns and structures from time series data, providing high-quality features for subsequent modeling. For example, for latency indicators, it might calculate the mean, standard deviation, and p99 quantile for different time windows; for resource usage indicators, it might extract peak utilization, rate of change in utilization, and periodic patterns; and for quality indicators, it might focus on deviations from baselines, short-term fluctuations, and long-term trends.

[0196] The data processed through feature engineering forms a monitoring dataset, a structured, multi-dimensional time-series dataset containing raw values ​​and derived features of various indicators, as well as time information and possible labels (such as known system states or anomaly markers). The monitoring dataset serves as the data foundation for subsequent state modeling and anomaly detection, and its quality directly affects the effectiveness of model monitoring.

[0197] Next, based on the monitoring dataset, a multi-component Gaussian Mixture Model (GMM) is trained using a pre-defined expectation-maximization algorithm to identify typical system operating states. A Gaussian Mixture Model (GMM) is a probabilistic model that assumes data is generated by a mixture of multiple Gaussian distributions, each representing a potential data generation mechanism or state. In model monitoring, GMMs can be used to model different operating states, such as low load, normal load, high load, or abnormal states. The multi-component GMM extends the basic GMM by allowing the model to include multiple components (i.e., multiple Gaussian distributions), each potentially corresponding to a typical system operating state.

[0198] The Expectation-Maximization (EM) algorithm is a standard method for training Generative Models (GMMs). It is an iterative algorithm that alternates between an expectation step (E-step) and a maximization step (M-step) to find the maximum likelihood estimate of the model parameters. In the E-step, the algorithm estimates the probability (responsibility) of each data point belonging to each component using the current parameters; in the M-step, the model parameters (mean vector, covariance matrix, and mixture weights) are updated based on these responsibilities to maximize the likelihood function. The EM algorithm starts with initial parameter estimates (possibly random initialization or using K-means clustering results) and iterates until convergence (parameter changes are less than a threshold or the maximum number of iterations is reached).

[0199] Determining the optimal number of components during training is a key challenge; that is, determining how many Gaussian distributions the mixture model should include. Too few components may fail to capture the complex structure of the data, while too many components can lead to overfitting. Common methods include information criteria (such as the Bayesian Information Criterion (BIC) or the Akaike Information Criterion (AIC), cross-validation, or domain-knowledge-based judgments. Multiple models with different numbers of components are trained, and then the optimal model that balances complexity and fit quality is selected.

[0200] For a given optimal number of components, the GMM is trained further, learning three key parameters for each component: a mean vector (representing the component's central location in the feature space), a covariance matrix (describing the component's shape and orientation), and mixture weights (representing the component's relative importance in the mixture). These parameters fully define the distributional characteristics of the mixture model, enabling it to represent complex multimodal data distributions. After training, the typical operating state represented by each component is identified, which may be based on the correspondence between component parameters and known states, or inferred by analyzing the characteristics of the data points captured by the component. For example, a low-latency, low-resource-utilization component may represent a "light load" state, while a high-latency, high-resource-utilization component may represent a "heavy load" or "resource-constrained" state.

[0201] The trained multi-component Gaussian mixture model provides a probabilistic framework for evaluating the likelihood of new monitoring data points belonging to any known state, or for identifying outliers that do not conform to any known state. This probabilistic approach is more flexible than simple threshold rules and can handle complex patterns and natural variations in multidimensional data.

[0202] Finally, based on the trained multi-component Gaussian mixture model, a mapping relationship between components and system states is established, and normal state boundaries are defined, forming monitoring configuration data and initial state data. The component-to-system-state mapping relationship associates each Gaussian component in the GMM with a system state of business and technical significance. This mapping can be manually defined based on expert knowledge or automatically inferred through analysis of component characteristics. For example, by analyzing the characteristic distribution of data points captured in each component, components are automatically labeled as "normal load," "peak load," "resource-constrained," or "inefficient state," etc. The mapping relationship makes the output of the statistical model interpretable, facilitating understanding and response to monitoring results by operations and maintenance personnel.

[0203] The normal state boundary defines the probabilistic boundary for normal operation and is used for anomaly detection. In the GMM framework, this is typically achieved by setting a probability density threshold or a Mahalanobis distance threshold. Specifically, an anomaly is defined as any data point whose log-likelihood value is below a certain threshold, or whose Mahalanobis distance to the nearest component center is greater than a certain threshold. The threshold can be set based on historical data analysis, business tolerance, or statistical significance levels, and a balance needs to be struck between false positives (misclassifying normal points as anomalies) and false negatives (failing to detect true anomalies). The normal state boundary can be global (applicable to all components) or local (each component has a unique boundary); the latter usually describes the range of normal variation under different states more accurately.

[0204] Monitoring configuration data is a set of structured information containing the complete configuration of the monitoring system, such as the definition of collection metrics, collection frequency settings, feature engineering parameters, GMM model parameters (number of components, mean vector, covariance matrix, and mixture weights), and anomaly detection thresholds. This configuration data enables the system to operate according to predetermined strategies and adjusts as conditions change. Monitoring configuration data is typically stored as configuration files or database records for easy version control and configuration management.

[0205] Initial state data serves as the baseline data for the system under normal operating conditions. It includes baseline values ​​for each indicator, normal fluctuation ranges, characteristic descriptions of typical states, and expected patterns of state transitions. This data provides a reference point for subsequent state monitoring and anomaly detection. Initial state data is a snapshot of the model's initial deployment, reflecting behavioral characteristics under ideal or expected conditions. As operation and the environment change, initial state data may need to be updated to reflect the new "normal," which is part of the continuous learning process in model monitoring.

[0206] This monitoring framework, based on multi-dimensional metrics, hierarchical data collection, and Gaussian mixture models, provides comprehensive and intelligent runtime monitoring capabilities for machine learning models. It not only detects anomalies but also understands different operating states and state transition patterns, providing a data foundation for predictive maintenance and performance optimization. Through unified monitoring configuration and initial state data, a manageable and scalable monitoring infrastructure is established, supporting full lifecycle management of models from deployment to retirement. This monitoring approach integrates traditional IT monitoring with machine learning-specific needs, making it particularly suitable for enterprise-level machine learning applications with high performance and quality requirements.

[0207] like Figure 2 As shown in the embodiments of this application, an integrated processing system for deploying machine learning models is provided. The system includes: The model file acquisition module 201 is used to acquire the model files uploaded by the user; The parsing module 202 is used to identify the framework type and call the corresponding parser based on the file characteristics of the model file, and to parse the framework model and the parser to determine the model parsing report, wherein the model parsing report includes model structured data and dependency information; The scheme configuration module 203 is used to determine the recommended parameter scheme based on the model parsing report and the deployment target environment selected by the user, and to verify the validity of the recommended parameter scheme to obtain the parameter configuration scheme; The environment deployment module 204 is used to generate an environment preparation script and a standardized model file after standardization transformation based on the parameter configuration scheme and the dependency information; generate an interface file based on the model structured data and the parameter configuration scheme; and form an environment deployment configuration file based on the parameter configuration scheme and the deployment target environment. The deployment data construction module 205 is used to construct a deployment data package based on the environment preparation script, the standardized model file, the interface file, and the environment deployment configuration file; The environment data acquisition module 206 is used to acquire the user's target environment credentials, which are used to indicate the environment connection status. The evaluation module 207 is used to perform automated deployment based on the target environment credentials and the deployment data package to obtain deployment results, and to perform operational quality evaluation based on the deployment results and the parameter configuration scheme to obtain service quality evaluation results. Based on the service quality evaluation results, the parameter configuration scheme and the deployment data package are optimized.

[0208] The system of this application embodiment can execute the method provided in this application embodiment. The implementation principle is similar. The actions performed by each module in the system of each embodiment of this application correspond to the steps in the method of each embodiment of this application. For detailed functional descriptions of each module of the system, please refer to the descriptions in the corresponding methods shown above, which will not be repeated here.

[0209] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An integrated processing method for deploying machine learning models, characterized in that, include: Retrieve the model file uploaded by the user; The framework type is identified and the corresponding parser is invoked based on the file characteristics of the model file. The framework model and the parser are then parsed to determine the model parsing report, which includes the model's structured data and dependency information. Based on the model analysis report and the deployment target environment selected by the user, a parameter recommendation scheme is determined, and the validity of the parameter recommendation scheme is verified to obtain a parameter configuration scheme. Based on the parameter configuration scheme and the dependency information, an environment preparation script and a standardized model file after standardization are generated. Based on the model structured data and the parameter configuration scheme, an interface file is generated. Based on the parameter configuration scheme and the deployment target environment, an environment deployment configuration file is formed. Based on the environment preparation script, the standardized model file, the interface file, and the environment deployment configuration file, a deployment data package is constructed; Obtain the user's target environment credentials, which are used to indicate the environment connection status; Based on the target environment credentials and the deployment data package, automated deployment is performed to obtain deployment results. Based on the deployment results and the parameter configuration scheme, an operational quality assessment is conducted to obtain a service quality assessment result. Based on the service quality assessment result, the parameter configuration scheme and the deployment data package are optimized.

2. The method according to claim 1, characterized in that, The process of identifying the framework type and calling the corresponding parser based on the file characteristics of the model file, parsing the framework model and the parser, and determining the model parsing report includes: The model file is subjected to security verification to obtain verification results, which include file type verification results, size limit check results, and virus scan results. After the verification result is passed, the model file is saved to a temporary storage area to generate a corresponding identifier, and the model file path and model meta-information are obtained. Based on the model file path and the model meta information, the initial judgment frame type is identified by the file extension, and the initial judgment frame type is identified by a preset scene loading model. The corresponding parser is selected to obtain the frame type and the parser instance corresponding to the parser. The scene loading model is used to indicate the loading of secondary confirmation in a preset fuzzy scene. Based on the framework type and the parser instance, model structured data is generated, which includes network structure, computational characteristic information, and feature information. Based on the model file and the framework type, the model meta information is scanned to extract framework version dependencies, hardware dependencies, and environment configuration requirements, and corresponding dependency information is generated. Based on the structured data of the model and the dependency information, a model parsing report is generated. The step of generating structured model data based on the framework type and the parser instance includes: Based on the framework type and the parser instance, a preset framework API is called to extract network structure information, which includes network layer type, input and output tensor shape, and number of parameters. Based on the network structure information, the computational characteristics of the model are determined, including the number of floating-point operations and computational complexity. Based on the network structure information, the feature information of the model is extracted, including the input feature dimension, the output feature dimension, and the feature type. Based on the network structure information, the computational characteristic information, and the feature information, the model structured data is generated.

3. The method according to claim 1, characterized in that, Based on the model analysis report and the user-selected deployment target environment, a parameter recommendation scheme is determined, and the validity of the parameter recommendation scheme is verified to obtain a parameter configuration scheme, including: Based on the model analysis report, analyze the configurable parameter items; Based on the preset model type and preset framework characteristics, a configuration interface is generated, and the default values ​​and optional ranges are displayed on the configuration interface. Based on the configurable parameter items, the configuration interface, the default values ​​and the optional ranges, an interactive parameter configuration interface is generated and displayed. The system obtains the deployment target environment selected by the user on the interactive parameter configuration interface, and obtains the environment resource characteristics, including GPU type and memory capacity. Based on the target deployment environment and the characteristics of the environmental resources, the environmental constraints are queried to obtain the corresponding deployment environment information; Based on the model analysis report and the deployment environment information, the matching degree between the model's computational characteristics and the environment's resource characteristics is analyzed using a preset parameter optimization model, and configuration parameters are predicted. Based on the model's computational characteristics, the matching degree, and the predicted configuration parameters, a parameter recommendation scheme is obtained, wherein the configuration parameters include resource configuration parameters and runtime configuration parameters. Based on the parameter recommendation scheme, the configuration parameters are verified to meet resource constraints and verification performance requirements, corresponding verification results are generated, and adjustment suggestions are generated based on the verification results to obtain target parameter configuration data. Based on the target parameter configuration data, static deployment parameters and dynamic operation parameters are integrated to generate a parameter configuration scheme.

4. The method according to claim 1, characterized in that, Based on the parameter configuration scheme and the dependency information, an environment preparation script and a standardized model file after standardization are generated; based on the model structured data and the parameter configuration scheme, an interface file is generated; and based on the parameter configuration scheme and the target deployment environment, an environment deployment configuration file is formed, including: Based on the dependency information and parameter configuration scheme in the model parsing report, the environment preparation script is generated by initializing the environment script and then encapsulating it. Based on the model parsing report and the parameter configuration scheme, an intermediate representation format suitable for the target environment is selected, and the model file is converted into a standardized model file according to the intermediate representation format using a preset call conversion model. Based on the input and output feature information in the model analysis report and the parameter configuration scheme, define the service API interface and generate the corresponding interface file based on the service API interface; Based on the parameter configuration scheme and the target deployment environment, select the corresponding environment deployment template, and form an environment deployment configuration file according to the environment deployment template.

5. The method according to claim 1, characterized in that, The deployment data package is constructed based on the environment preparation script, the standardized model file, the interface file, and the environment deployment configuration file, including: Based on the environment preparation script, the standardized model file, the interface file, and the environment deployment configuration file, create a deployment package directory structure; Based on the deployment package directory structure, the environment preparation script is copied to the script directory, the standardized model file is copied to the model directory, the interface file is copied to the interface directory, and the environment deployment configuration file is copied to the configuration directory to generate the deployment package directory; Based on the deployment package directory, generate container build files; Based on the deployment package directory and the container build file, a compression and packaging operation is performed to obtain the deployment data package.

6. The method according to claim 1, characterized in that, The process involves performing automated deployment based on the target environment credentials and the deployment data package to obtain deployment results, conducting operational quality assessments based on the deployment results and the parameter configuration scheme to obtain service quality assessment results, and optimizing the parameter configuration scheme and the deployment data package based on the service quality assessment results, including: Based on the environment connection status and the deployment data packet, the deployment file is unpacked, and the corresponding environment API is called to perform the deployment operation to obtain the deployment result; Based on the deployment results and the parameter configuration scheme, the monitoring system data is configured, and the collection indicators and collection frequency are set. Based on the monitoring system data, the collection indicators and the collection frequency, the monitoring configuration data and initial state data are obtained. Based on the monitoring configuration data and the initial state data, model service operation data is collected, and periodic quality assessments are performed to obtain service quality assessment results. Based on the service quality assessment results and the model service operation data, performance anomalies and quality deviations are detected, corresponding alarm information and optimization suggestions are generated, and the parameter configuration scheme and the deployment data package are optimized according to the optimization suggestions and the optimized service status.

7. The method according to claim 6, characterized in that, The step of extracting network structure information by calling a preset framework API based on the framework type and the parser instance includes: Based on the network structure information, the original feature information is extracted, which includes feature name, data type and dimension; Based on the original feature information, a corresponding mutual information calculation engine is selected based on the variable type. The variable type includes continuous variables, discrete-continuous variable pairs, and discrete variables. The mutual information calculation engine includes a preset k-nearest neighbor-based mutual information estimator selected for the continuous variable pairs, a preset histogram-based mutual information estimator selected for the discrete-continuous variable pairs, and a preset frequency-based mutual information estimator selected for the discrete variable pairs. Based on the mutual information calculation engine, the mutual information value between each original feature information and the target variable is calculated, and the feature importance ranking is generated based on the mutual information value to obtain the corresponding enhanced feature importance report; The network structure information is updated based on the enhanced feature importance report.

8. The method according to claim 7, characterized in that, The step of updating the network structure information based on the enhanced feature importance report includes: Based on the enhanced feature importance report, a mutual information matrix between features is calculated, and based on the mutual information matrix, highly correlated feature groups are identified, so that a feature correlation matrix is ​​obtained from the highly correlated feature groups; Based on the feature correlation matrix, a feature dependency graph is constructed using a preset graph theory model, wherein each node in the feature dependency graph represents the original feature information, and the edge weights in the feature dependency graph represent the mutual information values. Community detection is performed on the feature dependency graph to identify feature subsets whose correlation is greater than a preset correlation threshold, and the centrality index of each node is calculated based on the feature subsets to obtain feature structure information. The network structure information is updated based on the structured feature information and the enhanced feature importance report.

9. The method according to claim 8, characterized in that, Based on the model analysis report and the deployment environment information, a preset parameter optimization model is used to analyze the matching degree between the model's computational characteristics and the environment's resource characteristics, and configuration parameters are predicted. Based on the model's computational characteristics, the matching degree, and the predicted configuration parameters, a parameter recommendation scheme is obtained, including: Based on the historical deployment data obtained in advance, a parameter performance mapping matrix is ​​constructed, wherein each row of the parameter performance mapping matrix represents a set of historical deployment data, and each column of the parameter performance mapping matrix represents a performance index. Based on the parameter performance mapping matrix, a corresponding penalty form is selected according to the type parameter to determine the hybrid non-convex penalty function. The type parameter includes discrete resource allocation parameters, continuous optimization parameters, and parameters that need to be grouped. The parameters that need to be grouped include memory parameters and concurrency parameters. The penalty form includes truncated L1 penalty corresponding to the discrete resource allocation parameters, smooth shearing absolute deviation penalty corresponding to the continuous optimization parameters, and grouping MCP penalty corresponding to the parameters that need to be grouped. Based on the hybrid nonconvex penalty function, an objective function and a corresponding optimization objective are constructed. The optimization objective is used to indicate minimizing the weighted sum of the performance prediction error of the objective function and the nonconvex penalty term. Based on the optimization objective, the objective function is solved using a preset coordinate descent model or a preset proximal gradient model to obtain a sparse parameter weight matrix. Based on the model analysis report, the model computation characteristics are extracted, and based on the model computation characteristics, the model computation requirements information is obtained. The model computation characteristics include the number of floating-point operations, the number of parameters, and the memory requirements. Based on the deployment environment information, environmental resource characteristics are extracted, and environmental resource information is obtained based on the environmental resource characteristics. Based on the model calculation demand information and the environmental resource information, the parameter optimization model is used to generate the matching degree between the model calculation characteristics and the environmental resource characteristics, and a matching degree score corresponding to the matching degree is obtained. Based on the matching score and the sparse parameter weight matrix, the parameter optimization model is used to predict static deployment parameters and dynamic operation parameters to obtain the parameter recommendation scheme.

10. The method according to claim 9, characterized in that, The step of using the parameter optimization model to predict static deployment parameters and dynamic operation parameters based on the matching score and the sparse parameter weight matrix to obtain the parameter recommendation scheme includes: Based on the sparse parameter weight matrix, the parameter value range is determined according to environmental constraints and model characteristics, and a parameter search space is constructed based on the parameter value range; Based on the sparse parameter weight matrix and the parameter search space, the pre-screening only considers combinations of non-zero weight parameters to obtain a simplified parameter space. Based on the simplified parameter space, a multi-objective optimization model is constructed, which is used to indicate the simultaneous minimization of inference latency, the maximization of throughput, the maximization of resource utilization efficiency, and the maintenance of specified model accuracy. Based on the multi-objective optimization model, the Pareto optimal solution set is searched to obtain multiple sets of optional parameter configuration data; Based on the multiple sets of optional parameter configuration data and preset user performance preference data, the parameter recommendation scheme is generated.

11. The method according to claim 10, characterized in that, Based on the deployment results and the parameter configuration scheme, the monitoring system data is configured, and the collection indicators and collection frequency are set. Based on the monitoring system data, the collection indicators, and the collection frequency, monitoring configuration data and initial state data are obtained, including: Based on the monitoring system data, the collection indicators, and the collection frequency, multidimensional indicators are obtained, including computing performance indicators, resource utilization indicators, quality indicators, and business indicators. Based on the multidimensional indicators, a hierarchical acquisition strategy is determined, which includes a high-frequency acquisition key delay indicator acquisition strategy, a mid-frequency acquisition resource utilization indicator acquisition strategy, and a low-frequency acquisition quality evaluation indicator acquisition strategy. Based on the hierarchical acquisition strategy, time series data on the multidimensional indicators are collected, and feature engineering processing is performed on the time series data to obtain a monitoring dataset. Based on the monitoring dataset, a multi-component Gaussian mixture model is trained using a preset expectation-maximization algorithm, and the optimal number of components is determined. For each component, the mean vector, covariance matrix, and mixture weights are learned, and the typical working state represented by each component is identified, thus forming a trained multi-component Gaussian mixture model. Based on the trained multi-component Gaussian mixture model, a mapping relationship from components to system states is established, and normal state boundaries are defined. Based on the mapping relationship and the normal state boundaries, monitoring configuration data and initial state data are obtained.

12. An integrated processing system for deploying machine learning models, characterized in that, include: The model file acquisition module is used to acquire model files uploaded by users; The parsing module is used to identify the framework type and call the corresponding parser based on the file characteristics of the model file, and to parse the framework model and the parser to determine the model parsing report, wherein the model parsing report includes the model's structured data and dependency information; The scheme configuration module is used to determine the recommended parameter scheme based on the model parsing report and the deployment target environment selected by the user, and to verify the validity of the recommended parameter scheme to obtain the parameter configuration scheme. The environment deployment module is used to generate an environment preparation script and a standardized model file after standardization transformation based on the parameter configuration scheme and the dependency information; generate an interface file based on the model structured data and the parameter configuration scheme; and form an environment deployment configuration file based on the parameter configuration scheme and the deployment target environment. The deployment data construction module is used to construct a deployment data package based on the environment preparation script, the standardized model file, the interface file, and the environment deployment configuration file; The environment data acquisition module is used to acquire the user's target environment credentials, which are used to indicate the environment connection status. The evaluation module is used to perform automated deployment based on the target environment credentials and the deployment data package to obtain deployment results, and to perform operational quality evaluation based on the deployment results and the parameter configuration scheme to obtain service quality evaluation results. Based on the service quality evaluation results, the parameter configuration scheme and the deployment data package are optimized.

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