AI Model Registry With Sample Predictions for Ensemble Selection
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Solution Overview
Problem
Existing model registries lack efficient and accurate techniques for comparing performance of different AI models, leading to suboptimal model selection, excessive resource demands, and poor task performance due to the absence of a uniform metric for model comparison and identification of complementary models for ensembling.
Innovation Solution
Implementing a model registry with a uniform metric using sample predictions for each model, enabling efficient and accurate performance comparisons, and automatically suggesting ensembles based on customizable requirements such as resource demands and compute times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If model registries store only metadata without sample predictions, then storage requirements are reduced, but model performance comparison capability deteriorates
Solution Approach 1:
The patent extracts only the essential sample predictions needed for performance comparison from the full model output, storing just these critical samples alongside metadata. This selective extraction enables accurate model comparison while minimizing storage requirements compared to storing complete model outputs or extensive test results.
Solution Approach 2:
The system performs preliminary evaluation of models against benchmark datasets and stores the resulting sample predictions in advance. This preliminary action enables future performance comparisons without requiring re-execution of full model evaluations, thus reducing on-demand computational and storage burdens.
2Measurement precision
If comprehensive model evaluation is performed for each model, then model selection accuracy is improved, but computational resources consumed increase
Solution Approach 1:
The system performs comprehensive model evaluation in advance during the model registration phase, storing sample predictions and performance metrics. This preliminary action enables accurate model selection without requiring exhaustive re-evaluation of all models during the selection process, significantly reducing on-demand computational resource consumption.
Solution Approach 2:
Instead of re-executing full model evaluations, the system uses stored sample predictions and performance metrics as copies of the essential evaluation results. These pre-computed representations enable accurate model comparison and selection while avoiding the computational cost of repeating comprehensive evaluations.
3Reliability
If models are evaluated on diverse benchmark datasets, then model comparison reliability is improved, but data processing complexity increases
Solution Approach 1:
The patent segments the evaluation process by maintaining separate benchmark datasets and their corresponding sample predictions for different tasks. This segmentation allows reliable multi-dataset evaluation while managing complexity through organized, task-specific evaluation pipelines that process and store results independently.
Solution Approach 2:
The system implements a universal evaluation framework that handles multiple benchmark datasets through a common architecture. This multi-functional approach enables reliable model comparison across diverse datasets while reducing overall processing complexity through shared evaluation logic and standardized data structures.
4Adaptability or versatility
If manual model selection process is used, then flexibility in model choice is improved, but time consumption increases
Solution Approach 1:
The system provides feedback by automatically generating performance rankings and comparisons based on stored sample predictions. This feedback mechanism enables users to quickly identify promising models without manually evaluating each one, reducing time consumption while maintaining flexibility through customizable selection criteria.
Solution Approach 2:
The system performs preliminary ranking and organization of models based on their performance on benchmark datasets, storing this information alongside sample predictions. This preliminary action creates ready-to-use model recommendations that reduce the time needed for manual selection while preserving user flexibility to adjust criteria as needed.
Data Source
AI summary
Uploaded data including an artificial intelligence (AI) model and metadata associated with the AI model may be received. A task corresponding to the AI model may be determined. A processing device may confirm the metadata includes samples predictions generated based on benchmark data associated with the task corresponding to the AI model. The AI model and the sample predictions may be stored in an AI model registry in response to confirming the metadata includes the sample predictions generated based on the benchmark data associated with the task corresponding to the AI model.