LLM-Based ML Model Selection Platform for Entity Matching
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Solution Overview
Problem
Enterprises face challenges in selecting and configuring machine learning (ML) models for production use, particularly in automating tasks such as matching entities represented by computer-readable records, where ML models behave differently for each subset of data and require periodic retraining to maintain accuracy.
Innovation Solution
A ML model deployment platform that utilizes Large Language Models (LLMs) and few-shot learning to select and deploy ML models for production use. This platform generates few-shot examples using training and inference metrics, inputs these examples to LLMs for context, and provides user recommendations for model selection based on performance metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional ML model selection methods are used, then model deployment can be achieved, but the process is complex and requires significant human effort to evaluate multiple metrics and determine optimal configurations
Solution Approach 1:
The patent introduces LLMs as an intermediary component that mediates between the complex ML model metrics data and the user's selection decision. The LLM processes training metrics, inference metrics, and configuration parameters, transforming this complex information into simplified natural language recommendations. This intermediary layer shields users from the underlying complexity while maintaining deployment capability.
Solution Approach 2:
The patent replaces the traditional mechanical/manual process of model selection (where users manually evaluate metrics and make decisions) with an AI-based system using LLMs. The LLM automatically analyzes performance metrics and generates recommendations, substituting human cognitive effort with automated intelligent processing, thereby simplifying the operation while managing complexity through algorithmic sophistication.
2Measurement precision
If multiple ML models are trained and evaluated with comprehensive metrics, then deployment accuracy improves, but the time and resources required for model selection increase
Solution Approach 1:
The patent performs preliminary actions by pre-computing and storing comprehensive training metrics and inference metrics for multiple ML models before the actual selection process. This pre-processing of data enables the LLM to quickly retrieve and analyze relevant information during model selection, maintaining high selection accuracy while reducing the time required at the point of deployment decision.
Solution Approach 2:
The patent creates structured copies of model performance data in the form of standardized metric sets (training metrics, inference metrics, configuration parameters) that can be efficiently processed by the LLM. These copied and organized data representations enable rapid analysis without sacrificing the precision of the original comprehensive measurements, allowing fast comparison across multiple models.
3Ease of operation
If LLMs with few-shot learning are used for model selection, then user effort is reduced, but the system requires sophisticated prompt engineering and context management
Solution Approach 1:
The patent performs preliminary action by pre-structuring the prompt context with relevant few-shot examples and organizing metric data before presenting it to the LLM. This pre-prepared context includes sample model selections with their rationale, which guides the LLM's reasoning process. By preparing this context in advance, the system reduces the operational complexity for users while managing the sophisticated prompt requirements through systematic data organization.
Solution Approach 2:
The patent implements self-service by enabling the LLM to automatically generate model selection recommendations based on the provided metrics and few-shot examples, without requiring users to manually craft complex prompts or understand the underlying complexity. The system serves itself by using the structured data and pre-configured few-shot examples to drive the LLM's reasoning, thereby simplifying the user interface while managing complexity internally through automated context management.
Data Source
AI summary
Methods, systems, and computer-readable storage media for providing, for a set of ML models, a set of training metrics determined using test data during a training phase, providing, for a production-use ML model, a set of inference metrics based on predictions generated by the production-use ML model, generating, by a prompt generator, a set of few-shot examples using the set of training metrics and the set of inference metrics, inputting, by the prompt generator, the set of few-shot examples to a LLM as prompts, transmitting, to the LLM a query, displaying, to a user, a recommendation that is received from the LLM and responsive to the query, receiving input from a user indicating a user-selected ML model responsive to the recommendation, and deploying a user-selected ML model to an inference runtime for production use.