Model Registry Proxy Routing for Automated ML Version Serving
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Solution Overview
Problem
Existing machine learning model updating processes require substantial manual maintenance, increasing the likelihood of errors due to the need for manual creation of new endpoints and configuration of requesting services.
Innovation Solution
A system for machine learning model registry and serving that automates the process of updating models by synchronizing the model server with the model registry, using an interface to receive model versions, storing them, starting processes, and updating proxies with version information to redirect requests to the appropriate processes, thereby minimizing manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual model updating process is used, then model versions can be updated, but substantial manual maintenance is required and errors are more likely to be introduced
Solution Approach 1:
The system enables self-service model version management through an automated registry that tracks model versions and a synchronization mechanism that automatically updates model servers. When a new model version is registered, the system automatically detects the change, updates the model server, and manages endpoint routing without requiring manual intervention, thereby reducing errors and improving reliability.
Solution Approach 2:
The system implements feedback through a synchronization mechanism that continuously monitors the model registry for new versions and automatically triggers updates on model servers. This closed-loop feedback system ensures that model servers always have the latest versions and that endpoint configurations are automatically adjusted, eliminating manual configuration errors.
2Productivity
If manual endpoint creation and configuration is performed, then new model versions can be deployed, but the process becomes complex and time-consuming
Solution Approach 1:
The system performs preliminary action by pre-configuring the synchronization mechanism and endpoint routing logic in advance. When a new model version is registered, the system is already prepared to automatically detect the change, retrieve the model, update the server, and adjust endpoint routing. This eliminates the need for manual endpoint creation and configuration during deployment, significantly improving productivity while reducing complexity.
Solution Approach 2:
The system introduces an intermediary synchronization mechanism that acts as a mediator between the model registry and model servers. This intermediary automatically manages the deployment process by detecting new versions, coordinating the update process, and managing endpoint routing. This intermediary layer abstracts away the complexity of manual configuration while enabling rapid automated deployment.
3Ease of operation
If static production URLs are used, then request routing is simplified, but dynamic model version updates require manual endpoint reconfiguration
Solution Approach 1:
The system applies dynamics by making the endpoint routing configuration dynamic rather than static. While production URLs remain static for simplicity, the system dynamically updates the routing logic to point to new model versions automatically. The synchronization mechanism detects new versions and automatically adjusts endpoint configurations, combining the simplicity of static URLs with the flexibility of dynamic version updates.
Solution Approach 2:
The system achieves universality by creating an automated update mechanism that handles multiple functions: detecting new model versions, retrieving models from the registry, updating model servers, and reconfiguring endpoint routing. This multi-functional automated system works universally across all model version updates, eliminating the need for manual reconfiguration while maintaining static production URLs.
Data Source
AI summary
A system includes an interface, a processor, and a memory. The interface is configured to receive a version of a model from a model registry. The processor is configured to store the version of the model, start a process running the version of the model, and update a proxy with version information associated with the version of the model, wherein the updated proxy indicates to redirect an indication to invoke the version of the model to the process. The memory is coupled to the processor and configured to provide the processor with instructions.