Croissant: A Metadata Format for ML-Ready Datasets.

Mubashara Akhtar, Omar Benjelloun, Costanza Conforti, Pieter Gijsbers, 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, Jos van der Velde, Steffen VoglerCarole-Jean Wu

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

3 Citaten (Scopus)
13 Downloads (Pure)

Samenvatting

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.

Originele taal-2Engels
TitelDEEM '24
SubtitelProceedings of the Eighth Workshop on Data Management for End-to-End Machine Learning
UitgeverijAssociation for Computing Machinery, Inc
Pagina's1-6
Aantal pagina's6
ISBN van elektronische versie9798400706110
ISBN van geprinte versie979-8-4007-0611-0
DOI's
StatusGepubliceerd - 9 jun. 2024

Bibliografische nota

DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

Financiering

Joan Giner-Miguelez is supported by the AIDOaRt project, which is funded by the ECSEL Joint Undertaking (JU) under grant agreement No 101007350. The JU receives support from the European Union s Horizon 2020 research and innovation programme and Sweden, Austria, Czech Republic, Finland, France, Italy, and Spain. Pieter Gijsbers, Joaquin Vanschoren, and Jos van der Velde would like to acknowledge funding by EU s Horizon Europe research and innovation program under grant agreement No. 952215 (TAILOR) and No. 101070000 (AI4EUROPE).

FinanciersFinanciernummer
European Union’s Horizon Europe research and innovation programme
Electronic Components and Systems for European Leadership101007350
European Union’s Horizon Europe research and innovation programme101070000, AI4EUROPE, 952215

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