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

The emergence of advanced artificial intelligence (AI) models has driven the development of frameworks and approaches that focus on automating model training and hyperparameter tuning of end-to-end AI pipelines. However, other crucial stages of these pipelines such as dataset selection, feature engi...

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Bibliographic Details
Main Authors:	Revathy Venkataramanan, Aalap Tripathy, Tarun Kumar, Sergey Serebryakov, Annmary Justine, Arpit Shah, Suparna Bhattacharya, Martin Foltin, Paolo Faraboschi, Kaushik Roy, Amit Sheth
Format:	Article
Language:	English
Published:	Frontiers Media S.A. 2025-01-01
Series:	Frontiers in Big Data
Subjects:	AI pipeline metadata graph learning graph recommendation AIMKG metadata knowledge graphs AI pipeline optimization
Online Access:	https://www.frontiersin.org/articles/10.3389/fdata.2024.1476506/full
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Constructing a metadata knowledge graph as an atlas for demystifying AI pipeline optimization

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