Authors: Mubashara Akhtar, Omar Benjelloun, Costanza Conforti, Joan Giner-Miguelez, Nitisha Jain, Michael Kuchnik, Quentin Lhoest, Pierre Marcenac, Manil Maskey, Peter Mattson, Luis Oala, Pierre Ruyssen, Rajat Shinde, Elena Simperl, Goeffry Thomas, Slava Tykhonov, Joaquin Vanschoren, Steffen Vogler, Carole-Jean Wu

Published on: March 28, 2024

Impact Score: 7.8

Arxiv code: Arxiv:2403.19546

Summary

• What is new: Introduction of Croissant, a new metadata format for datasets designed to facilitate the usage of data in ML tools and frameworks.
• Why this is important: Data is essential for machine learning, but managing and integrating data across various tools presents a key challenge.
• What the research proposes: Croissant, a metadata format that enhances dataset discoverability, portability, and interoperability across ML tools and repositories.
• Results: Croissant has been adopted by several dataset repositories, covering hundreds of thousands of datasets, making them readily usable in popular ML frameworks.

Technical Details

Technological frameworks used: Croissant metadata format integration

Models used: nan

Data used: Dataset repositories supporting Croissant

Potential Impact

Data repository platforms, cloud storage services, AI and machine learning development tools

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