Shadow ML Model UAT Using Production Inference Data
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Solution Overview
Problem
There is a data sparsity problem in validating trained machine learning (ML) models prior to deployment to production environments, especially for new ML models, leading to potential failures due to differences in behavior from existing models, and existing testing methods are inadequate for ensuring reliability and trust in ML systems.
Innovation Solution
An automated user acceptance test (UAT) system that evaluates ML models in an inactive state in parallel with production environments, providing passive inference results for evaluation before transitioning them to an active state, using production data without disrupting ongoing operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If past validated scenarios are used to test new ML models, then testing can be performed with existing data, but the test data is not large enough to represent production data and cannot validate the automation rate of new models
Solution Approach 1:
The system performs preliminary testing of new ML models in a UAT environment before full production deployment. A shadow ML model is trained and evaluated in advance using production data in the UAT environment, allowing validation of model performance before it impacts actual production operations. This preliminary action ensures models are thoroughly tested with sufficient data representation.
Solution Approach 2:
The UAT environment serves as an intermediary between development and production. It provides a intermediate testing space where new ML models can be validated with production-like data without directly impacting production systems. The shadow ML model acts as an intermediary that mirrors production behavior for testing purposes.
2Productivity
If new ML models are deployed directly to production, then deployment speed is improved, but model behavior differences can cause production failures
Solution Approach 1:
The system performs preliminary validation of new ML models in the UAT environment before production deployment. The shadow ML model is trained and evaluated in advance to assess model performance and detect potential issues. This preliminary action enables faster safe deployment by pre-validating models rather than requiring extensive post-deployment monitoring.
Solution Approach 2:
The UAT environment with shadow ML models provides a cushioning layer between model development and production deployment. It absorbs potential failures by detecting model issues before they reach production, preventing direct impact on production stability while maintaining deployment speed.
3Measurement precision
If ML models are tested in parallel with production environments using production data, then model accuracy is improved, but technical overhead increases
Solution Approach 1:
The system creates a shadow ML model that copies the structure and behavior of the production ML model for testing purposes. This copy allows accurate evaluation of model performance using production data without requiring complex testing infrastructure. The shadow model replicates production behavior to enable precise accuracy measurement.
Solution Approach 2:
The UAT environment serves multiple functions: it validates new ML models, trains shadow ML models for comparison, and provides a testing platform using production data. This multi-functionality reduces the need for separate specialized testing systems, thereby reducing overall system complexity while maintaining measurement precision.
Data Source
AI summary
Methods, systems, and computer-readable storage media for receiving, by a ML application executing within a cloud platform, a first inference request, the first inference request including first inference data, transmitting, by the ML application, the first inference data to the UAT system within the cloud platform, retrieving, by the UAT system, a first ML model in response to the inference request, the first ML model being in an inactive state, providing, by the UAT system, a first inference based on the first inference data using the first ML model, providing a first accuracy evaluation at least partially based on the first inference, and transitioning the first ML model from the inactive state to an active state, the first ML model being used for production in the active state.