ML Pipeline Segment Reuse for Faster High-Value Model Building
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Solution Overview
Problem
Creating a machine learning pipeline is a time-consuming and iterative process that relies heavily on the experience of the data scientist, and techniques for efficient creation and configuration of such pipelines are lacking, especially in identifying and applying high-value pipeline segments that satisfy target performance levels.
Innovation Solution
A method for discovering and reusing high-value machine learning pipeline segments by defining datasets based on data operations, generating a library of pipeline segments, and calculating quality and evolution degrees to identify and recommend segments for reuse, leveraging metadata and position information for efficient identification and understanding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a machine learning pipeline is created manually by a data scientist, then the pipeline can be customized and optimized for specific problems, but the process becomes time-consuming and heavily dependent on the data scientist's experience
Solution Approach 1:
The patent segments the machine learning pipeline into reusable pipeline segments that can be independently identified, stored in a library, and reused. Each segment represents a modular unit of data processing operations that can be independently evaluated and reused across different projects, reducing the time required to create new pipelines while maintaining effectiveness.
Solution Approach 2:
The patent performs preliminary action by pre-processing and evaluating pipeline segments to determine their reusability characteristics before they are needed. The system pre-calculates quality metrics, evolution degrees, and reusability scores for pipeline segments, so that when a new pipeline is needed, pre-validated segments can be quickly selected and reused without requiring extensive manual creation time.
2Productivity
If pipeline segments are reused from existing pipelines, then development efficiency is improved, but identifying high-value segments requires complex evaluation of data characteristics and quality
Solution Approach 1:
The patent implements feedback mechanisms that automatically evaluate pipeline segments based on multiple criteria including data quality metrics, evolution degrees, and reusability characteristics. The system provides feedback scores and rankings for each segment, guiding the selection process. This automated feedback system reduces the complexity of identifying high-value segments by transforming complex evaluation criteria into actionable scoring metrics.
Solution Approach 2:
The patent replaces manual mechanical evaluation of pipeline segments with automated computational analysis. Instead of manually assessing data characteristics and quality metrics, the system uses automated algorithms to calculate evolution degrees, quality scores, and reusability metrics, significantly reducing the complexity of segment identification while improving productivity.
3Manufacturing precision
If comprehensive evaluation metrics are used to identify high-value pipeline segments, then the quality of reused segments is ensured, but the processing overhead increases
Solution Approach 1:
The patent applies partial action by implementing a multi-tier evaluation system where not all pipeline segments undergo complete comprehensive evaluation. Instead, the system performs initial filtering based on basic criteria, then applies more intensive evaluation metrics only to segments that pass the initial filter or are identified as potential high-value candidates. This approach maintains segment selection accuracy while reducing overall processing overhead by avoiding exhaustive evaluation of every segment.
Data Source
AI summary
Computer-implemented methods for discovery and reuse of a high value pipeline segment are provided. Aspects include defining a set of datasets associated with a processing pipeline based on a set of data operations of the processing pipeline. Aspects also include generating a library of pipeline segments based on the processing pipeline and at least one dataset of the set of datasets. In some aspects, generating the library of pipeline segments includes adding a pipeline segment of the processing pipeline to the library based on one or more characteristics of a dataset generated by the pipeline segment, where the dataset is included in the set of datasets.