ML Pipeline Artifact Comparison for Fuzzy Regression Testing
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Solution Overview
Problem
Traditional regression testing methods for machine learning models are ineffective due to the probabilistic and non-deterministic nature of ML outputs, making it difficult to verify the impact of changes in supporting packages on model performance.
Innovation Solution
A system and method for ML pipeline testing that involves generating and comparing artifacts from different versions of the pipeline using reference artifacts, allowing for flexible threshold-based evaluations to ensure stability and functionality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional regression testing methods are used for machine learning models, then the testing process is simple and straightforward, but the testing is ineffective due to the probabilistic and non-deterministic nature of ML outputs
Solution Approach 1:
The patent segments the ML pipeline into discrete components (data preprocessing, model training, inference, supporting packages) and creates separate artifact representations for each component. This segmentation allows targeted testing of individual components while maintaining the ability to test the entire pipeline, resolving the contradiction by making the testing system manageable yet comprehensive.
Solution Approach 2:
The patent introduces artifacts as intermediary representations that capture the state and behavior of ML components without requiring direct execution. These artifacts serve as mediators between the probabilistic ML outputs and deterministic testing requirements, enabling effective regression testing while managing complexity through abstraction.
2Reliability
If supporting packages are updated to fix bugs, address security vulnerabilities, or enable new features, then the functionality and security of the ML pipeline are improved, but the stability of ML outputs may be affected due to the probabilistic nature of ML models
Solution Approach 1:
The patent performs preliminary artifact generation and comparison before deploying package updates to production. By testing artifacts from updated packages against baseline artifacts in advance, the system can identify potential output stability issues before they affect production ML models, allowing proactive resolution while maintaining improved functionality.
Solution Approach 2:
The patent implements a feedback mechanism where artifact comparison results guide whether package updates should be deployed. If artifact differences exceed acceptable thresholds, the system provides feedback to prevent deployment or trigger further investigation, thereby maintaining output stability while still allowing beneficial package updates to proceed.
3Measurement precision
If strict equality checking is used to compare ML outputs across different pipeline versions, then the testing criteria are clear and objective, but the testing fails to account for statistical variations in probabilistic ML outputs
Solution Approach 1:
The patent changes the comparison parameter from strict equality to threshold-based similarity measurement. Instead of requiring exact matches, the system compares artifacts within acceptable tolerance thresholds that account for statistical variations. This maintains measurement precision through quantifiable criteria while improving testing reliability by accommodating the probabilistic nature of ML outputs.
Data Source
AI summary
Systems and methods for fuzzy regression testing of a machine learning pipeline. The pipeline includes one or more supporting software packages and executes a machine learning model. Reference artifacts associated with the pipeline are obtained. Subsequently, one or more test script is executed to compare test artifacts generated during execution to the reference artifacts.