ML Model Catalog for Embedding-Space Fit Evaluation
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Solution Overview
Problem
Existing machine learning models produce embedding spaces that are minimally understood by users, and there is a lack of systematic tools to determine their generalizability to new datasets, especially when labeled data is sparse, leading to challenges in identifying the best-suited model for a given dataset.
Innovation Solution
A computer system and method that utilize an intelligent ML model catalog to evaluate user datasets against pre-created training data spaces of embeddings, employing topological data analysis and computational geometry to assess model fit through intrinsic and extrinsic evaluations, providing recommendations based on efficiency, robustness, and generalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pre-trained deep learning models are used for representation learning, then model performance and generalizability are improved, but understanding and interpretability of the embedding spaces deteriorate
Solution Approach 1:
The patent introduces an intermediary evaluation system that acts as a mediator between the black-box pre-trained models and users. This system includes automated evaluation metrics, visualization tools, and recommendation algorithms that translate complex embedding space characteristics into interpretable insights, allowing users to understand model behavior without needing deep expertise in the underlying mathematics
Solution Approach 2:
The patent implements feedback mechanisms through automated evaluation frameworks that provide continuous information about model performance on user datasets. The system evaluates embedding quality using multiple metrics, provides actionable recommendations for model selection and fine-tuning, and enables iterative improvement by comparing different pre-trained models against user-specific criteria
2Adaptability or versatility
If manual fine-tuning of models is performed to improve use case adaptability, then model suitability for specific applications is improved, but time and computational resources required are increased
Solution Approach 1:
The patent performs preliminary actions by pre-evaluating multiple pre-trained models against user datasets before actual deployment. The system computes embedding spaces, evaluates them using automated metrics, and generates recommendations in advance, eliminating the need for time-consuming manual fine-tuning and trial-and-error experimentation
Solution Approach 2:
The patent enables self-service through automated model evaluation and recommendation systems that autonomously assess model suitability without requiring expert intervention. The system automatically computes embeddings, evaluates performance metrics, compares multiple models, and provides actionable recommendations, allowing users to select appropriate models without manual fine-tuning expertise
3Measurement precision
If comprehensive model evaluation frameworks are implemented, then model selection accuracy is improved, but system complexity and computational overhead are increased
Solution Approach 1:
The patent segments the comprehensive evaluation framework into distinct modular components: embedding extraction modules, evaluation metric modules (intrinsic and extrinsic), visualization modules, and recommendation modules. Each component performs a specific function and can be independently configured, allowing users to select only the evaluation metrics and analyses relevant to their specific needs without being overwhelmed by the entire framework
Data Source
AI summary
A computer system is configured to provide an intelligent machine-learning (ML) model catalog containing data associated with multiple ML models. The multiple ML models are trained over multiple training datasets respectively, and the intelligent ML model catalog contains at least multiple training data spaces of embeddings generated based on the multiple ML models and the multiple training datasets. In response to receiving a user dataset, for at least one ML model in the plurality of ML models, the computer system is configured to extract a user data space of embeddings based on the at least one ML model and the user dataset, and evaluate the user data space against the training data space to determine whether the at least one ML model is a good fit for the user dataset.