MLAAS Platform Automating Model Training via Inference Feedback
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Solution Overview
Problem
Current machine learning integration in software products is complex and inefficient, as developers face challenges in collecting, validating, and processing datasets, leading to suboptimal or inaccurate modeling due to the lack of focus on machine learning architectures for dataset gathering and connection.
Innovation Solution
A machine learning-as-a-service (MLAAS) system that includes a data store and a cognitive processes system for training and performing inference using machine learning models, which receives feedback to iteratively improve model accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If developers manually integrate machine learning functionality, then they can implement custom models, but the process becomes complicated and time-consuming
Solution Approach 1:
The patent introduces a service infrastructure as an intermediary layer between developers and machine learning systems. This infrastructure includes automated data collection services, dataset management services, and model deployment services that handle the complex integration tasks. Developers can request model integration through standardized interfaces, and the infrastructure automatically manages the complicated processes of data collection, validation, feature extraction, and model deployment, thus resolving the contradiction between custom model implementation and integration complexity.
Solution Approach 2:
The system implements self-service capabilities where the machine learning infrastructure automatically performs data collection, validation, and model training without requiring manual developer intervention for each step. The automated services can independently gather relevant data from multiple sources, validate datasets, extract features, and deploy models. This self-service mechanism reduces the complexity burden on developers while still enabling custom model implementation through automated workflows.
2Reliability
If developers manually collect and validate datasets, then they can ensure data quality, but the process becomes lengthy and resource-intensive
Solution Approach 1:
The patent implements preliminary action by pre-configuring data collection pipelines and validation rules before actual model development begins. The service infrastructure maintains pre-approved data sources, pre-defined validation schemas, and pre-established feature extraction methods. When a model integration is requested, the system can immediately begin data collection using pre-configured pipelines, significantly reducing the time required while maintaining data quality through pre-established validation mechanisms.
Solution Approach 2:
The system implements continuous data collection and validation processes that operate automatically in the background. Rather than requiring developers to manually collect and validate datasets from scratch for each project, the infrastructure maintains continuous data pipelines that constantly gather, validate, and prepare datasets. This continuous action ensures data quality is maintained while eliminating the lengthy manual processes, as the system is already engaged in useful data preparation activities continuously.
3Measurement precision
If comprehensive data validation is performed, then model accuracy improves, but the processing time increases
Solution Approach 1:
The patent applies partial action by implementing selective data validation strategies. Rather than performing exhaustive validation on all possible data aspects, the system identifies and validates only the critical features and data elements most relevant to the specific model and task. The automated services can adjust the depth and scope of validation based on the model requirements, validating essential data thoroughly while performing lighter validation on less critical aspects. This partial validation approach maintains model accuracy for key predictions while reducing overall processing time compared to comprehensive validation of all data elements.
Data Source
AI summary
A modular machine learning-as-a-service (MLAAS) system uses machine learning to respond to tasks without requiring machine learning modeling or design knowledge by its users. The MLAAS system receives an inference request including a model identifier and a target defining features for use in processing the inference request. The features correspond to a task for evaluation using a machine learning model associated with the model identifier. An inference outcome is generated by processing the inference request using the target as input to the model. Feedback indicating an accuracy of the inference outcome with respect to the task is later received and used to generate a training data set, which the MLAAS can use to further train model used to generate the inference outcome. As a result, the training of a machine learning model by the MLAAS system is limited to using data resulting from an inference performed using that model.