ML Model Testing System with Validation Layer
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Solution Overview
Problem
Current techniques for testing machine learning models are inefficient due to the lack of validated test data and the extensive range of test data, leading to incorrect training and testing, which consumes computing resources and results in incorrect predictions.
Innovation Solution
A testing system that receives a machine learning model, historical validated test data, and real-time data, performs unit tests, regression tests, scale tests, and longevity tests to calculate model scores, identify inference delays, and update the model based on test results, scores, and resource utilization, ensuring correct functionality and resource optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If extensive test data is used for testing machine learning models, then testing coverage is improved, but computing resource consumption increases
Solution Approach 1:
The system performs preliminary validation of test data using multiple testing approaches (unit tests, regression tests, scale tests, longevity tests) before using the data for model training. This preliminary action ensures data quality and validity, preventing wasted computing resources on incorrect or invalid test data later in the process.
Solution Approach 2:
The system introduces an intermediary validation layer between test data collection and model training. This intermediary performs comprehensive tests including unit tests, regression tests, scale tests, and longevity tests to filter and validate test data, ensuring only high-quality data proceeds to the training phase, thus optimizing resource utilization.
2Measurement precision
If test data validation is performed thoroughly, then prediction accuracy is improved, but testing time increases
Solution Approach 1:
The validation process is segmented into multiple independent testing phases: unit tests for individual components, regression tests for model stability, scale tests for performance under different conditions, and longevity tests for sustained operation. This segmentation allows parallel execution and targeted validation, reducing overall testing time while maintaining comprehensive coverage.
Solution Approach 2:
The system applies multiple layers of testing (unit, regression, scale, longevity) that may seem excessive but are designed to catch different types of errors at different stages. By distributing validation efforts across multiple specialized tests rather than one comprehensive test, the system achieves thorough validation more efficiently.
3Productivity
If machine learning models are trained with unvalidated test data, then training speed is improved, but model reliability deteriorates
Solution Approach 1:
The system performs preliminary validation of test data through multiple testing approaches (unit tests, regression tests, scale tests, longevity tests) before using the data for model training. This preliminary action ensures data quality and validity, preventing wasted computing resources on incorrect or invalid test data later in the process.
Solution Approach 2:
The system introduces an intermediary validation layer between test data collection and model training. This intermediary performs comprehensive tests including unit tests, regression tests, scale tests, and longevity tests to filter and validate test data, ensuring only high-quality data proceeds to the training phase, thus optimizing resource utilization.
Data Source
AI summary
A device may receive a machine learning model, training data, and test data, and may perform a unit test on the machine learning model to generate unit test results. The device may perform regression tests on the machine learning model, with the training data and the test data, to calculate model scores, create graphs, determine inference delays, and identify missing points for the machine learning model. The device may perform scale and longevity tests on the machine learning model, with the training data and the test data, to identify additional missing points and calculate a resource utilization for the machine learning model. The device may update the machine learning model, to generate an updated machine learning model, based on the unit test results, the model scores, the graphs, the inference delays, the missing points, the additional missing points, or the resource utilization.