Prediction Model Testing Framework Shadow Deployment
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Solution Overview
Problem
The challenge lies in effectively testing new prediction models in a production environment without disrupting existing systems, as traditional testing platforms struggle to evaluate performance accurately and efficiently, especially when the new model's predictions should not drastically differ from the current model's outputs.
Innovation Solution
A prediction model testing system is implemented with a test environment that mirrors the production environment, allowing for the collection and replication of metrics data to compare the performance of a new prediction model under test (PMUT) with the existing production model, generating reports on prediction errors and ensuring minimal deviation in predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a new prediction model is deployed in production environment, then prediction accuracy may be improved, but system stability and reliability deteriorate due to unpredictable performance variations
Solution Approach 1:
The patent implements a shadow deployment mechanism where the new prediction model is pre-configured and runs in parallel with the existing model before full deployment. This preliminary action allows the system to evaluate the new model's performance and stability without immediately replacing the proven model, thus improving prediction accuracy while maintaining system stability during the transition period.
Solution Approach 2:
The patent introduces a model comparison module as an intermediary that continuously monitors and compares predictions from both the existing model and the new model. This mediator evaluates performance metrics, detects anomalies, and determines when the new model is ready for full deployment, thereby bridging the gap between improved accuracy and maintained reliability.
2Ease of operation
If traditional testing platforms are used to evaluate new prediction models, then testing process is simpler, but evaluation accuracy and comprehensiveness deteriorate due to inability to capture real-world performance
Solution Approach 1:
The patent creates a shadow environment that replicates the production environment's data flow, metrics collection, and operational conditions. This copy allows the new prediction model to be tested with real-world data and workloads, providing accurate evaluation of its performance while maintaining the simplicity of a structured testing process through automated data capture and comparison.
3Reliability
If new prediction model is tested extensively before deployment, then reliability of deployment is improved, but time and cost of testing increase
Solution Approach 1:
The patent implements continuous monitoring and evaluation of the new prediction model during shadow deployment, rather than relying on discrete, time-consuming testing phases. The system continuously compares predictions, tracks performance metrics, and accumulates evidence of reliability in real-time, enabling rapid validation without sacrificing deployment reliability.
Solution Approach 2:
The patent enables the new prediction model to serve itself during the evaluation period by having it process real production data and automatically generate performance reports. The model's own predictions are evaluated against actual outcomes, providing self-validation that accelerates the reliability assessment process while maintaining rigorous standards.
Data Source
AI summary
A prediction model testing system includes a test environment that is used to test a prediction model under test (PMUT). A metrics collector in a production environment collects and stores production metrics data generated from computing resources in a production environment. A production predictor in the production environment generates production predictions for the metrics, using a production prediction model. A test manager may make the production metrics data available in a test environment. Test predictions are generated in the test environment from the metrics data using the PMUT. The test manager may then calculate respective prediction errors of the production prediction model and the PMUT, and generate a report indicating the differences between the two sets of prediction errors. The report may be used by the test management system to determine whether a test of the PMUT was successful.