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Constructing a metadata knowledge graph as an atlas for demystifying AI pipeline optimization.

Revathy Venkataramanan1,2, Aalap Tripathy2, Tarun Kumar2

  • 1AI Institute, University of South Carolina, Columbia, SC, United States.

Frontiers in Big Data
|January 22, 2025
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Summary
This summary is machine-generated.

This study introduces the AI Pipeline Metadata Knowledge Graph (AIMKG) to efficiently manage and utilize metadata from AI pipelines. AIMKG enhances AI pipeline search and recommendations, improving efficiency and discoverability in AI development.

Keywords:
AI pipeline metadataAI pipeline optimizationAIMKGgraph learninggraph recommendationmetadata knowledge graphs

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Area of Science:

  • Artificial Intelligence
  • Machine Learning Engineering
  • Data Science

Background:

  • Automating AI model training and hyperparameter tuning is advanced, but other pipeline stages like dataset selection and feature engineering are less optimized.
  • Improving end-to-end AI pipeline efficiency necessitates metadata from past executions, which is computationally challenging to regenerate.
  • Existing AI metadata is fragmented across diverse open-source platforms, posing integration and unification challenges.

Purpose of the Study:

  • To address the challenge of integrating and unifying AI pipeline metadata from diverse sources.
  • To introduce a solution for sourcing and managing AI pipeline metadata for improved efficiency.
  • To construct a comprehensive knowledge graph for AI pipeline metadata to aid in search and recommendation.

Main Methods:

  • Sourced AI pipeline metadata from open-source platforms like Papers-with-Code, OpenML, and Hugging Face.
  • Introduced the Common Metadata Ontology (CMO) to unify diverse terminologies and data formats.
  • Constructed an extensive AI Pipeline Metadata Knowledge Graph (AIMKG) with 1.6 million pipelines and applied semantic enhancements.

Main Results:

  • The AI Pipeline Metadata Knowledge Graph (AIMKG) was constructed, containing 1.6 million pipelines.
  • Quantitative evaluation showed a custom aggregation model achieved 76.3% retrieval accuracy (R@1), outperforming baselines.
  • Qualitative analysis demonstrated AIMKG-based recommendations were relevant in 78% of cases, surpassing the MLSchema-based recommender (51%).

Conclusions:

  • AIMKG serves as a valuable resource for navigating the AI landscape, providing practitioners with insights for AI pipeline optimization.
  • The knowledge graph facilitates data mining and analysis of evolving AI workflows.
  • AIMKG significantly improves the search and recommendation of relevant AI pipelines, enhancing overall AI development efficiency.