Shadow Model Testing for Machine Learning Deployment
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Solution Overview
Problem
Existing machine learning model testing in offline environments is suboptimal due to the use of synthesized or stale data, leading to models lagging behind current trends and not being adequately tested before deployment in production environments.
Innovation Solution
Implementing a system with both production and shadow machine learning models on a model serving platform, where input data is routed to both types, allowing shadow models to be tested in a production environment with real data and promoting or demoting models based on their performance, enabling continuous retraining and optimal model deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If models are tested in offline environments, then model stability is maintained, but testing accuracy deteriorates due to synthesized or stale data
Solution Approach 1:
The patent introduces shadow models as intermediary testing entities that operate in the production environment but do not affect actual production operations. These shadow models receive copies of production data and generate predictions that are evaluated separately, allowing accurate testing with real data while maintaining production stability through the mediation layer.
Solution Approach 2:
The system performs preliminary testing by running shadow models alongside production models before fully deploying new models. This preliminary action allows the system to evaluate model performance with real production data in advance, identifying potential issues before they affect actual production operations.
2Productivity
If new models are deployed quickly to production, then productivity increases, but model reliability decreases due to inadequate testing
Solution Approach 1:
The system performs preliminary testing by running shadow models alongside production models before fully deploying new models. This preliminary action allows the system to evaluate model performance with real production data in advance, identifying potential issues before they affect actual production operations.
Solution Approach 2:
The system implements continuous feedback loops where shadow model predictions are compared against actual production outcomes. This feedback mechanism provides real-time performance metrics that guide model deployment decisions, allowing rapid iteration while maintaining reliability through data-driven validation.
3Measurement precision
If shadow models receive 100% of input data, then testing comprehensiveness improves, but system complexity increases
Solution Approach 1:
The system creates simplified copies of the production model architecture as shadow models. These shadow models replicate the essential structure and logic of production models but operate in isolation for testing purposes. This copying approach enables comprehensive testing with real data while managing complexity through modular, independent test instances.
Data Source
AI summary
In some aspects, the techniques described herein relate to a method including: providing, on a model serving platform, a plurality of production machine learning models and a plurality of shadow machine learning models; routing input data to the plurality of production machine learning models and to the plurality of shadow machine learning models; receiving, at a model monitoring engine, production output data from a first production machine learning model of the plurality of production machine learning models; receiving, at the model monitoring engine, offline output data from a first shadow machine learning model of the plurality of shadow machine learning models; promoting the first shadow machine learning model to a production machine learning model based on the offline output data; and demoting the first production machine learning model based on the production output data.