Meta-Dataset Model Selection for Reliable ML Deployment
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Solution Overview
Problem
Existing systems lack an efficient method for evaluating and selecting machine-learning models for given datasets, particularly in terms of performance and adaptability to new data, which is crucial for ensuring long-term reliability.
Innovation Solution
A method involving the execution of multiple machine-learning models on datasets, generating labels for best performers, extracting profiles, forming a meta dataset, and using a gradient boosted tree model to select the most suitable model based on quantitative and qualitative assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple machine-learning models are executed and evaluated on datasets to ensure long-term reliability, then model selection accuracy is improved, but computational time and resources increase
Solution Approach 1:
The system performs preliminary evaluation of multiple machine-learning models on training datasets before deployment, generating performance labels and extracting dataset profiles in advance. This preliminary action creates a meta dataset that captures model performance characteristics, enabling faster selection later without re-evaluating all models from scratch.
Solution Approach 2:
The system creates a meta dataset that is a condensed representation or copy of the full model evaluation results. Instead of storing and re-processing all original model outputs, the meta dataset captures essential performance characteristics, allowing rapid model selection while preserving the reliability benefits of comprehensive evaluation.
2Reliability
If multiple machine-learning models are executed and evaluated on datasets to ensure long-term reliability, then model selection accuracy is improved, but computational resources increase
Solution Approach 1:
The system extracts only the essential performance characteristics and dataset profiles from comprehensive model evaluations, separating the critical selection criteria from the full evaluation process. This extraction creates a streamlined meta dataset that maintains selection accuracy while reducing the computational resources needed for actual model selection.
Solution Approach 2:
The system transforms the original model evaluation parameters into a condensed meta dataset format, changing the representation of model performance data. This parameter transformation reduces computational resource requirements by working with aggregated performance metrics rather than raw evaluation outputs.
3Adaptability or versatility
If dataset profiles are extracted and associated with performance labels to form a meta dataset, then model adaptability to new data is improved, but system complexity increases
Solution Approach 1:
The system merges dataset profiles with model performance labels into a unified meta dataset structure. This combination integrates multiple data types (profile characteristics and performance metrics) into a single coherent framework, improving model adaptability while managing complexity through unified data organization.
Solution Approach 2:
The meta dataset structure serves multiple functions: it stores dataset characteristics, model performance metrics, and selection criteria in a single universal format. This multi-functionality reduces the need for separate data structures and processing pipelines, thereby reducing overall system complexity while enhancing adaptability.
Data Source
AI summary
The present disclosure describes a method including receiving a plurality of datasets, executing a plurality of machine-learning models on each of the plurality of datasets, generating, for each of the plurality of datasets, a label identifying a best performing one of the plurality of machine-learning models, the best performing one of the plurality of machine-learning models being evaluated based on performance evaluations derived from executing the plurality of the machine-learning models on a same one of the plurality of datasets, extracting a set of profiles from each of the plurality of datasets, associating the label with the set of profiles of the same dataset for each of the plurality of datasets, generating a meta dataset from a plurality of label-associated sets of profiles, and running a estimating machine-learning model on the meta dataset to select one of the plurality of the machine-learning models as a trained machine-learning model.