Automated Regression Detection for Enterprise ML Models
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Solution Overview
Problem
Existing machine learning (ML) model retraining in enterprise applications often results in performance regression, requiring manual effort and resources for regression testing, which is not scalable in production environments that demand frequent training jobs.
Innovation Solution
An automated regression detection system (ARDS) monitors ML model training, automatically conducts regression testing using Gaussian processes, and generates results, including notifications to stakeholders, to detect performance variance and improvements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual regression testing is performed for ML models, then performance validation can be conducted, but the process is not scalable and requires significant manual effort
Solution Approach 1:
The system enables self-service automation where the ARDS automatically monitors training completion, retrieves models, executes regression testing, generates results, and publishes findings without requiring manual intervention. The system serves itself by autonomously performing the entire regression testing workflow, thereby maintaining reliable performance validation while achieving scalability.
Solution Approach 2:
The patent replaces manual mechanical testing processes with an automated electronic system. The ARDS uses computer processors to execute regression testing algorithms, generate performance comparisons, and communicate results through notifications and graphical interfaces, substituting human manual operations with automated mechanical/electronic systems that can scale efficiently.
2Adaptability or versatility
If frequent ML model training jobs are executed, then the system can adapt to changing features, but performance regression may occur and requires intensive testing resources
Solution Approach 1:
The ARDS implements feedback mechanisms by automatically comparing retrained model performance against baseline performance using Gaussian process variance analysis. The system provides immediate feedback on performance changes through regression results and notifications, enabling stakeholders to respond to performance degradation or improvement in real-time, thus maintaining reliability while allowing frequent updates.
Solution Approach 2:
The system performs preliminary regression testing automatically upon training completion before the model is fully deployed. By conducting regression detection as a preliminary action, the system can identify potential performance issues early, allowing teams to address them before they affect production, thereby maintaining performance consistency despite frequent feature updates.
3Productivity
If automated regression testing is implemented, then scalability is improved, but the system complexity increases
Solution Approach 1:
The ARDS is designed as a universal system that performs multiple functions: monitoring training completion, retrieving models, executing regression testing using Gaussian processes, generating performance comparisons, and publishing results through various channels. By consolidating these functions into a single multi-functional system, the patent achieves scalability without proportionally increasing complexity.
Data Source
AI summary
Methods, systems, and computer-readable storage media for determining, by an automated regression detection system (ARDS), that training of a ML model is complete, the ML model being a version of a previously trained ML model, and in response, automatically, by the ARDS: retrieving the ML model, executing regression testing and detection using the ML model, generating regression results relative to the previously trained ML model, and publishing the regression results.