Automated ML Model Validation and Production Deployment
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Solution Overview
Problem
Users without data science knowledge face challenges in selecting appropriate machine learning models for specific tasks and optimizing their performance.
Innovation Solution
A collaborative platform that automatically identifies, trains, and deploys machine learning models tailored to user-specific tasks, utilizing a retrieval-augmented generation architecture for knowledge management and question-answering, with evaluation frameworks to enhance model performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If automated model selection and deployment is implemented, then ease of operation is improved for non-data scientists, but device complexity increases
Solution Approach 1:
The patent introduces an automated machine learning platform that acts as an intermediary between non-data-scientist users and complex ML model selection processes. The platform includes automated model selection components that evaluate multiple ML models, perform cross-validation, and select optimal models without requiring user expertise in data science, thereby resolving the contradiction between ease of operation and the inherent complexity of ML model selection
Solution Approach 2:
The system enables self-service automated model selection where the platform automatically performs model evaluation, validation, and selection based on user-defined criteria. The automated process includes generating performance metrics, conducting cross-validation, and deploying models without manual intervention from non-expert users, allowing the system to serve itself in resolving the technical contradiction
2Reliability
If comprehensive model evaluation and validation is performed, then reliability is improved, but loss of time increases
Solution Approach 1:
The patent implements preliminary automated model evaluation and validation processes that assess multiple ML models before deployment. The system performs cross-validation, generates performance metrics, and evaluates model reliability in advance, allowing users to make informed decisions without time-consuming manual testing later, thus resolving the contradiction between ensuring reliability and minimizing time loss
Solution Approach 2:
The automated validation process enables rapid evaluation of multiple models by skipping manual testing steps. The system quickly performs cross-validation, generates performance metrics, and identifies optimal models through automated workflows, reducing the time required for comprehensive model evaluation while maintaining reliability standards
3Manufacturing precision
If multiple machine learning models are evaluated and tested, then manufacturing precision is improved in model selection, but productivity decreases
Solution Approach 1:
The patent segments the model evaluation process into distinct automated components: model generation, cross-validation, performance metric generation, and model selection. Each component handles specific tasks independently, allowing parallel processing and automated workflow management that maintains high model selection precision while improving overall productivity through efficient task distribution
Data Source
AI summary
Systems, methods, and computer-readable media are disclosed for systems and methods for automated validation and deployment of machine learning models as a service. Example methods may include determining a first request for generation of a first machine learning model, automatically generating the first machine learning model using the first set of features, automatically validating the first machine learning model, deploying the first machine learning model in a production network environment, and updating the first machine learning model using the first set of feedback signals. Methods may include determining a second request for an artificial intelligence output via the graphical user interface, determining a data input associated with the second request, selecting, based on the data input, a first large language model from a set of large language models, generating the artificial intelligence output using the first large language model, and causing presentation of the artificial intelligence output.