Croissant: a metadata format for ML-Ready Datasets

Akhtar, Mubashara; Benjelloun, Omar; Conforti, Costanza; Gijsbers, Pieter; Giner Miguelez, Joan; Jain, Nitisha; Kuchnik, Michael; Lhoest, Quentin; Marcenac, Pierre; Maskey, Manil; Mattson, Peter; Oala, Luis; Ruyssen, Pierre; Shinde, Rajat; Simperl, Elena; Thomas, Goeff; Tykhonov, Vyacheslav; Vanschoren, Joaquin; van der Velde, Jos; Vogler, Steffen; Wu, Carole-Jean

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Título :	Croissant: a metadata format for ML-Ready Datasets
Autoría:	Akhtar, Mubashara Benjelloun, Omar Conforti, Costanza Gijsbers, Pieter Giner Miguelez, Joan Jain, Nitisha Kuchnik, Michael Lhoest, Quentin Marcenac, Pierre Maskey, Manil Mattson, Peter Oala, Luis Ruyssen, Pierre Shinde, Rajat Simperl, Elena Thomas, Goeff Tykhonov, Vyacheslav Vanschoren, Joaquin van der Velde, Jos Vogler, Steffen Wu, Carole-Jean
Citación :	Akhtar, M. [Mubashara], Benjelloun, O. [Omar], Conforti, C. [Costanza], Gijsbers, P. [Pieter], Giner-Miguelez, J. [Joan], Jain, N. [Nitisha],... & Wu, J.C. [Jean-Carole]. (2024). Croissant: a metadata format for ML-Ready Datasets. Proceedings of the 8th Workshop on Data Management for End-to-End Machine Learning, DEEM 2024 (p. 1-6). New York, NY: Association for computing machinery. doi: 10.1145/3650203.3663326
Resumen :	Data is a critical resource for Machine Learning (ML), yet working with data remains a key friction point. This paper introduces Croissant, a metadata format for datasets that simplifies how data is used by ML tools and frameworks. Croissant makes datasets more discoverable, portable and interoperable, thereby addressing significant challenges in ML data management and responsible AI. Croissant is already supported by several popular dataset repositories, spanning hundreds of thousands of datasets, ready to be loaded into the most popular ML frameworks.
Palabras clave :	ML datasets discoverability reproducibility responsible AI
DOI:	https://doi.org/10.1145/3650203.3663326
Tipo de documento:	info:eu-repo/semantics/conferenceObject
Versión del documento:	info:eu-repo/semantics/publishedVersion
Fecha de publicación :	9-jun-2024
Licencia de publicación:	http://creativecommons.org/licenses/by/3.0/es/
Datos relacionados:	https://dl.acm.org/doi/10.1145/3650203.3663326#core-collateral-metrics
Aparece en las colecciones:	Conferències Conference lectures