ML Model Guidance via Component Relation Mapping
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Solution Overview
Problem
Data scientists face challenges in identifying and leveraging existing machine learning models and data due to the sheer volume of generated data, leading to duplicated work and reduced efficiency, as manual recall of previous changes and experiences is difficult and infeasible.
Innovation Solution
A system that identifies, determines relations between, and suggests machine learning models based on their components, using a mapping of component relations to provide guidance and improve model selection and refinement, leveraging patterns in data and user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual recall of previous changes and experiences is used, then data scientists can identify existing models, but the sheer volume of generated data makes this process difficult and infeasible, leading to duplicated work
Solution Approach 1:
The patent replaces manual recall processes with automated computational systems. The system uses machine learning models to automatically identify existing models and their components by analyzing data patterns, relationships, and metadata, substituting human cognitive efforts with algorithmic processing that can handle large volumes of data efficiently.
Solution Approach 2:
The patent introduces an intermediary system that acts as a bridge between data scientists and existing models. This intermediary automatically indexes, catalogs, and retrieves model information based on component relationships and data characteristics, eliminating the need for direct manual search and recall by data scientists.
2Adaptability or versatility
If data scientists manually review existing models to avoid duplication, then they can leverage previous work, but the volume of data and models makes this process infeasible
Solution Approach 1:
The patent segments models into their constituent components (features, parameters, data sources, etc.) and creates an indexed structure of these components. This segmentation allows the system to efficiently search and match models by comparing specific components rather than reviewing entire models manually, making the process both adaptable and easy to operate.
Solution Approach 2:
The patent creates simplified representations or copies of model components and their relationships that can be quickly searched and compared. These copied structures serve as indexes that enable rapid identification of existing models without requiring direct examination of the full model implementations.
3Measurement precision
If the system analyzes component relations among models, then it can provide accurate suggestions, but this requires processing large volumes of data and model information
Solution Approach 1:
The patent performs preliminary analysis and indexing of model components before actual model selection or comparison tasks. By pre-processing and organizing component relationships in advance, the system reduces the complexity of real-time analysis while maintaining high accuracy in suggestions, as the heavy lifting of relationship mapping is already completed.
Solution Approach 2:
The patent transforms the analysis from examining entire models in one dimension to analyzing individual model components and their relationships across multiple dimensions. This dimensional transformation allows the system to manage complexity by breaking down complex model comparisons into simpler component-level relationships that can be processed more efficiently.
Data Source
AI summary
Techniques facilitating guiding machine learning models and related components are provided. In one example, a computer-implemented method comprises identifying, by a device operatively coupled to a processor, a set of models, wherein the set of models includes respective model components; determining, by the device, one or more model relations among the respective model components, wherein the one or more model relations respectively comprise a vector of component relations between respective pairwise ones of the model components; and suggesting, by the device, a subset of the set of models based on a mapping of the component relations.