Machine Learning Model Registry for Interoperable Version Deployment
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Solution Overview
Problem
Existing systems lack an efficient and automated method for deploying and updating machine learning models and access modules in a networked environment, particularly for providing prediction services such as home price estimation, while ensuring interoperability and performance validation.
Innovation Solution
A machine learning model registry system that periodically generates and validates new versions of machine learning models and access modules, performs acceptance tests, and automatically deploys them to server machines, ensuring interoperability and performance through a user interface for comparison and promotion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated deployment and validation of machine learning models is implemented, then productivity and reliability of prediction services are improved, but device complexity increases
Solution Approach 1:
The system performs self-validation through automated acceptance tests that automatically evaluate new model versions against validation datasets and performance criteria. The registry autonomously manages version registration, compatibility checking, and deployment validation without requiring manual intervention for each model update, enabling self-service operation that improves productivity while managing complexity through automation.
Solution Approach 2:
The system implements feedback mechanisms where validation results from acceptance tests are automatically fed back into the registry to determine whether new model versions should be deployed. Performance metrics and compatibility check results provide feedback loops that automatically adjust deployment decisions, improving reliability through continuous validation while managing complexity through systematic feedback processing.
2Reliability
If multiple versions of machine learning models and access modules are maintained for interoperability validation, then reliability is improved, but loss of time in deployment and validation increases
Solution Approach 1:
The system performs preliminary validation by maintaining multiple versions of models and access modules in the registry, conducting acceptance tests in advance before actual deployment. Compatibility and interoperability are validated beforehand using validation datasets, ensuring that when deployment occurs, the models have already been proven to work correctly together, thus improving reliability while managing validation time through advance preparation.
Solution Approach 2:
The system creates and maintains copies of different versions of machine learning models and access modules in the registry. These copies are used for validation purposes without affecting production systems, allowing parallel validation of multiple versions simultaneously. This copying approach enables comprehensive interoperability testing while avoiding sequential validation delays in the deployment pipeline.
3Manufacturing precision
If automated acceptance testing and validation are performed for each new model version, then manufacturing precision of model deployment is improved, but use of energy and computational resources increases
Solution Approach 1:
The system changes validation parameters by using different datasets for training versus validation purposes. Acceptance tests use specific validation datasets and performance criteria that are distinct from training data, allowing efficient evaluation of model improvements. This parameter separation enables precise deployment validation by focusing computational resources on evaluating specific performance metrics rather than retraining models, thus improving deployment accuracy while managing energy consumption.
Data Source
AI summary
Systems and methods to utilize a machine learning model registry are described. The system deploys a first version of a machine learning model and a first version of an access module to server machines. Each of the server machines utilizes the model and the access module to provide a prediction service. The system retrains the machine learning model to generate a second version. The system performs an acceptance test of the second version of the machine learning model to identify it as deployable. The system promotes the second version of the machine learning model by identifying the first version of the access module as being interoperable with the second version of the machine learning model and by automatically deploying the first version of the access module and the second version of the machine learning model to the plurality of server machines to provide the prediction service.