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 Vogler; Carole-Jean Wu

doi:10.1145/3650203.3663326

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

5 Citations (Scopus)

21 Downloads (Pure)

Abstract

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.

Original language	English
Title of host publication	DEEM '24
Subtitle of host publication	Proceedings of the Eighth Workshop on Data Management for End-to-End Machine Learning
Publisher	Association for Computing Machinery, Inc
Pages	1-6
Number of pages	6
ISBN (Electronic)	9798400706110
ISBN (Print)	979-8-4007-0611-0
DOIs	https://doi.org/10.1145/3650203.3663326
Publication status	Published - 9 Jun 2024

Bibliographical note

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.

Funding

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).

Funders	Funder number
European Union's Horizon 2020 - Research and Innovation Framework Programme
Electronic Components and Systems for European Leadership	101007350
European Union's Horizon 2020 - Research and Innovation Framework Programme	101070000, AI4EUROPE, 952215

Keywords

ML datasets
discoverability
reproducibility
responsible AI

Access to Document

10.1145/3650203.3663326

pdfFinal published version, 1.42 MB

Cite this

Akhtar, M., Benjelloun, O., Conforti, C., Gijsbers, P., Giner-Miguelez, J., Jain, N., Kuchnik, M., Lhoest, Q., Marcenac, P., Maskey, M., Mattson, P., Oala, L., Ruyssen, P., Shinde, R., Simperl, E., Thomas, G., Tykhonov, S., Vanschoren, J., van der Velde, J., ... Wu, C.-J. (2024). Croissant: A Metadata Format for ML-Ready Datasets. In DEEM '24: Proceedings of the Eighth Workshop on Data Management for End-to-End Machine Learning (pp. 1-6). Association for Computing Machinery, Inc. https://doi.org/10.1145/3650203.3663326

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Akhtar, M, Benjelloun, O, Conforti, C, Gijsbers, P, Giner-Miguelez, J, Jain, N, Kuchnik, M, Lhoest, Q, Marcenac, P, Maskey, M, Mattson, P, Oala, L, Ruyssen, P, Shinde, R, Simperl, E, Thomas, G, Tykhonov, S, Vanschoren, J , van der Velde, J, Vogler, S & Wu, C-J 2024, Croissant: A Metadata Format for ML-Ready Datasets. in DEEM '24: Proceedings of the Eighth Workshop on Data Management for End-to-End Machine Learning. Association for Computing Machinery, Inc, pp. 1-6. https://doi.org/10.1145/3650203.3663326

Croissant: A Metadata Format for ML-Ready Datasets. / Akhtar, Mubashara; Benjelloun, Omar; Conforti, Costanza et al.
DEEM '24: Proceedings of the Eighth Workshop on Data Management for End-to-End Machine Learning. Association for Computing Machinery, Inc, 2024. p. 1-6.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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PB - Association for Computing Machinery, Inc

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Croissant: A Metadata Format for ML-Ready Datasets.

Abstract

Bibliographical note

Funding

Keywords

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