Hybrid Machine Learning System via Component Merging
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Solution Overview
Problem
Current machine learning solutions face challenges in collaboration and optimization, as experts from different fields struggle to contribute effectively due to lack of awareness, collaboration, and cross-solution optimization, leading to suboptimal results and difficulty in identifying the best components of machine learning solutions.
Innovation Solution
A system and method for developing hybrid machine learning solutions by receiving and combining components from multiple submissions, scoring, and ranking them using a leaderboard, allowing for the creation of optimal hybrid solutions that outperform individual submissions by leveraging diverse expertise and data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple machine learning solutions are developed independently by experts from different fields, then diverse expertise and data sources are leveraged, but collaboration and cross-solution optimization are lacking leading to suboptimal results
Solution Approach 1:
The patent combines multiple independently developed machine learning solutions into a unified framework where solutions from different experts and fields are merged. This allows diverse expertise to be leveraged while enabling cross-solution optimization through systematic integration and comparison of multiple approaches.
Solution Approach 2:
The patent creates a universal platform that can accommodate and evaluate multiple machine learning solutions from different fields and expertise areas. This multi-functional system allows various types of ML solutions to be processed, compared, and optimized together, resolving the contradiction between diversity and optimization.
2Ease of operation
If machine learning solutions are developed without systematic comparison and ranking, then individual solutions can be created independently, but it becomes difficult to identify the best components and achieve optimal results
Solution Approach 1:
The patent implements a feedback mechanism through systematic scoring and ranking of machine learning solutions. Each solution is evaluated against defined criteria, and results are fed back to identify the best components. This allows independent development to continue while providing precise measurement and identification of optimal solutions through structured evaluation feedback.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, receiving a plurality of proposed machine learning solutions to a machine learning problem including receiving, for each respective proposed machine learning solution of the plurality of proposed machine learning solutions, one or more of a machine learning model, a dataset and a data pipeline output; automatically determining hybrid solutions to the machine learning problem, including combining, by the processing system, at least one of a first component from a first proposed machine learning solution with at least one of a second component from a second proposed machine learning solution; and ranking the hybrid solutions including determining a log loss score for each hybrid solution and sorting the hybrid solutions according to the log loss score for each hybrid solution. Other embodiments are disclosed.