ML/AI Model Selection Through Objective-Function Checkpoints
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Solution Overview
Problem
Selecting the appropriate machine learning (ML) and artificial intelligence (AI) model for deployment is challenging due to the need for a desired balance of model objectives, typically requiring cumbersome and resource-intensive trial and error processes like hyperparameter optimization, which are repeated from scratch when objectives change.
Innovation Solution
A method for ML/AI model selection based on specified objective functions, involving the creation of checkpoints during model development to track model versions and evaluate their suitability, allowing for efficient selection without restarting the process when objectives change.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If trial and error and hyperparameter optimization approaches are used for model selection, then model objectives can be balanced, but the process becomes cumbersome and resource intensive
Solution Approach 1:
The system performs preliminary evaluation of multiple ML/AI models against multiple objective functions before deployment is needed. By pre-computing performance metrics and storing results in a database, the system avoids repeated trial-and-error processes when objectives change, directly reducing selection complexity while maintaining reliability
Solution Approach 2:
The system evaluates models against multiple objective functions simultaneously (e.g., accuracy, fairness, robustness) rather than optimizing for a single objective. This multi-functional evaluation approach allows the same model selection infrastructure to serve different deployment scenarios, reducing overall complexity while improving selection accuracy
2Reliability
If hyperparameter optimization is performed at various intervals, then model performance can be optimized, but the process is repeated from scratch whenever objectives change
Solution Approach 1:
The system performs hyperparameter optimization and model evaluation in advance, storing optimized models and their performance characteristics in a database. When objectives change, the system retrieves pre-computed results and re-ranks existing models rather than repeating optimization from scratch, significantly reducing time loss while maintaining performance
Solution Approach 2:
Instead of discarding previously optimized models when objectives change, the system recovers and re-evaluates existing optimized models against new objective functions. This approach reuses computational investments while adapting to new requirements, reducing both time loss and resource waste
3Measurement precision
If multiple ML/AI models are evaluated against multiple objective functions, then comprehensive comparison is achieved, but computational resources increase
Solution Approach 1:
The system segments the model evaluation process into distinct phases: initial comprehensive evaluation of multiple models against multiple objective functions, followed by retrieval and re-ranking of pre-evaluated models when objectives change. This segmentation allows comprehensive comparison when needed while reducing energy consumption during routine selections
Solution Approach 2:
The system performs full comprehensive evaluation only when necessary (e.g., when no suitable model exists in the database or when objectives fundamentally change). For routine selections, it uses partial evaluation by re-ranking existing models, reducing computational energy while maintaining sufficient comparison accuracy
Data Source
AI summary
In one embodiment, an illustrative process herein may comprise: accessing, by a device, a plurality of machine learning models; determining, by the device, one or more objective functions for the plurality of machine learning models; evaluating, by the device, the plurality of machine learning models against the one or more objective functions to establish a comparative assessment of each of the plurality of machine learning models for the one or more objective functions; and providing, by the device, the comparative assessment of each of the plurality of machine learning models to a model selection process for selection of a specific machine learning model from the plurality of machine learning models for a given objective function of the one or more objective functions.