Machine Learning Model Validation via Reference Similarity
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Solution Overview
Problem
There is a need for a system to validate machine learning models to ensure they comply with specific rules and perform satisfactorily across various applications and devices, as existing methods lack comprehensive validation processes.
Innovation Solution
A method and system for validating a machine learning model by comparing its output against a plurality of machine learning models stored in a database, determining the degree of similarity, and ensuring compliance with validation rules based on predetermined thresholds, which can be performed in a cloud environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a machine learning model is validated by comparing against multiple machine learning models in a database, then the reliability and quality of model validation is improved, but the complexity of the validation system increases
Solution Approach 1:
The patent uses reference machine learning models stored in a database as copies or representatives of validated model patterns. Instead of validating each model individually through complex manual processes, the system creates a library of reference models that encode validation criteria, allowing new models to be validated by comparison against these stored copies, thereby improving reliability while managing system complexity.
Solution Approach 2:
The patent introduces an intermediary validation system that mediates between the machine learning model to be validated and the multiple reference models in the database. This intermediary component automatically performs similarity comparisons and compliance checks, acting as a mediator that simplifies the validation process for users while ensuring thorough multi-model comparison for reliable validation results.
2Manufacturing precision
If comprehensive validation rules are applied to ensure model compliance, then the quality of model performance is improved, but the time required for validation increases
Solution Approach 1:
The patent applies preliminary action by pre-storing multiple validated reference machine learning models in a database before the actual validation process. These reference models are prepared in advance with known validation characteristics, allowing the validation system to quickly compare new models against pre-established criteria encoded in the reference models, thereby reducing validation time while maintaining comprehensive quality checks.
Solution Approach 2:
The patent replaces manual or mechanical validation processes with an automated computational system. Instead of time-consuming manual review of model performance against validation rules, the system uses automated machine learning model comparisons and similarity algorithms to rapidly assess compliance, substituting human effort with efficient computational processes that maintain precision while reducing time loss.
3Productivity
If multiple machine learning models are compared simultaneously against a test model, then the efficiency of validation is improved, but the computational resources required increase
Solution Approach 1:
The patent applies segmentation by dividing the validation process into manageable components: the test model is compared against multiple reference models in sequential or prioritized batches rather than all at once. The database of reference models can be segmented into groups based on relevance or validation criteria, allowing the system to process comparisons in segments, thereby improving overall validation efficiency while managing computational resource consumption through controlled parallel processing.
Data Source
AI summary
Systems, methods, and computer-readable media are disclosed for validating a machine learning model. In one aspect, a machine learning model validation system can receive a test machine learning model, analyze an output of the test machine learning model, determine a degree of similarity between the test machine learning model and one or more machine learning models stored in a database based on the output of the test machine learning model, and determining whether the test machine learning model complies with a set of validation rules based on the degree of the similarity with respect to one or more thresholds.