ML Model Versioning via Function Map Comparison
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Solution Overview
Problem
Existing version control systems lack the ability to determine the significance of changes to machine learning models, as they are based on factors applicable to source code changes rather than model training processes, and fail to detect changes in model output values caused by updates.
Innovation Solution
Implementing a Regression Model Version Tagger (RMVT) that evaluates the significance of parameter changes in machine learning models by comparing updated and original function maps, using threshold values to classify updates as major or minor revisions, and deploying the updated model into a production environment with appropriate versioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing version control systems are used for machine learning models, then version tracking is implemented, but the systems fail to detect significant changes in model output values
Solution Approach 1:
The patent introduces function maps as an intermediary representation that captures the input-output behavior of machine learning models. By comparing function maps rather than raw model parameters, the system can accurately detect significant changes in model output values. The function map serves as a mediator that translates complex model transformations into comparable functional representations, enabling precise change detection.
Solution Approach 2:
The patent transforms the model comparison problem into a parameter change detection problem by representing models through function maps with specific parameters. By monitoring changes in function map parameters (such as input values, output values, and their relationships), the system can identify significant model updates. This parameter-based approach allows for quantitative assessment of model changes using threshold values.
2Adaptability or versatility
If source code-based version control is applied to machine learning models, then versioning is achieved, but the control mechanisms are based on factors applicable to source code rather than model training processes
Solution Approach 1:
The patent fundamentally changes the parameters used for version control from source code metrics (lines of code, file modifications) to model-functional parameters (function map comparisons, output value changes). This parameter transformation makes the version control system adaptable to machine learning models while maintaining precision in measuring actual model changes. The system now tracks parameters that reflect the true nature of model evolution during training processes.
Solution Approach 2:
Instead of controlling versions based on what changes in the model (source code, parameters), the patent inverts the approach by controlling versions based on what the model does (functional output). By comparing function maps that represent model behavior rather than model structure, the system achieves adaptability to ML models while precisely measuring the significance of changes in model output values.
3Productivity
If all model updates are deployed without classification, then deployment speed is maintained, but compatibility issues arise from undetected significant changes
Solution Approach 1:
The patent applies preliminary action by classifying model updates as major or minor revisions before deployment into the production environment. The function map comparison and significance determination occur in advance, allowing the system to prepare appropriate deployment strategies. This preliminary classification ensures that significant changes are properly managed before they reach production, preventing compatibility issues while maintaining deployment efficiency.
Solution Approach 2:
The patent implements feedback by using the results of function map comparisons to guide deployment decisions. The classification of updates as major or minor revisions provides feedback that informs the deployment process. This feedback mechanism ensures that significant changes are handled appropriately, maintaining production environment compatibility while allowing rapid deployment of minor updates. The system continuously monitors and adjusts deployment based on the significance of model changes.
Data Source
AI summary
An embodiment includes specifying an original model to be trained by a machine learning platform using training data and an algorithm, wherein the training data includes a plurality of training input values upon which to make a prediction and a plurality of training output values representing target prediction values. The embodiment generates an updated model by training the original model to algorithmically approach the training output values as provided with the training data based on the plurality of input values. The embodiment generates an updated function map representative of output values of the updated model and versions the updated model according to a classification rule for assigning a version classification based on a comparison of the updated function map to an original function map representative of output values of the original model. The embodiment deploys the updated model into a production environment. to replace the original model.