Model Registry Deployment With Automated Acceptance Testing
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Solution Overview
Problem
Existing systems lack an efficient and automated mechanism for deploying and updating machine learning models and access modules in a networked environment, particularly in applications like home valuation prediction services, without manual intervention and validation.
Innovation Solution
A machine learning model registry system that automates the deployment and validation of new versions of machine learning models and access modules, ensuring interoperability and performance through acceptance tests and user interface-driven updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual deployment and validation of machine learning models is used, then control and validation accuracy are improved, but deployment time and system complexity increase
Solution Approach 1:
The system performs self-validation by automatically executing acceptance tests against the new model version without requiring manual intervention. The validation process is autonomous, where the system tests the model itself against predefined criteria and promotes it automatically if it passes, eliminating the need for manual deployment while maintaining validation accuracy.
Solution Approach 2:
The system implements automated feedback loops where acceptance test results are automatically evaluated and used to determine model promotion. The validation process provides immediate feedback on model performance against predefined criteria, and this feedback automatically triggers deployment decisions without manual review, reducing deployment time while maintaining reliability.
2Productivity
If automated deployment mechanism is implemented, then deployment efficiency is improved, but system complexity increases
Solution Approach 1:
The system segments the deployment process into distinct, manageable components: model training, acceptance test execution, validation against predefined criteria, and promotion decisions. This segmentation allows each component to be independently developed and maintained, reducing overall system complexity while enabling automated deployment through clear separation of responsibilities.
Solution Approach 2:
The system performs preliminary actions by pre-defining acceptance criteria and test cases before model deployment. These predefined validation rules are established in advance, allowing the automated system to simply execute them against new models without requiring complex real-time decision logic, thereby improving deployment efficiency while keeping the validation system relatively simple.
3Speed
If model versioning and validation are automated, then deployment speed is improved, but validation thoroughness may decrease
Solution Approach 1:
The system ensures validation thoroughness through automated feedback from acceptance tests that are executed against every new model version. The predefined criteria provide comprehensive validation feedback that automatically evaluates model performance, ensuring that speed of deployment does not compromise validation thoroughness since the automated system rigorously tests each model against established standards.
Solution Approach 2:
The system replaces manual validation mechanisms with automated mechanical validation processes. Instead of human reviewers manually assessing models, the system uses automated acceptance test execution and predefined criteria evaluation, which provides consistent, thorough validation at the speed of machine execution, eliminating the trade-off between speed and thoroughness.
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.