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: Contribution to conference types › Paper › peer-review

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
Pages	1-6
Number of pages	6
DOIs	https://doi.org/10.1145/3650203.3663326
Publication status	Published - 9 Jun 2024
Event	8th Workshop on Data Management for End-to-End Machine Learning, DEEM 2024 - Santiago, Chile Duration: 9 Jun 2024 → …

Conference

Conference	8th Workshop on Data Management for End-to-End Machine Learning, DEEM 2024
Country/Territory	Chile
City	Santiago
Period	9/06/2024 → …

Keywords

discoverability
ML datasets
reproducibility
responsible AI

Access to Document

10.1145/3650203.3663326

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. 1-6. Paper presented at 8th Workshop on Data Management for End-to-End Machine Learning, DEEM 2024, Santiago, Chile. https://doi.org/10.1145/3650203.3663326

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author = "Mubashara Akhtar and Omar Benjelloun and Costanza Conforti and Pieter Gijsbers and Joan Giner-Miguelez and Nitisha Jain and Michael Kuchnik and Quentin Lhoest and Pierre Marcenac and Manil Maskey and Peter Mattson and Luis Oala and Pierre Ruyssen and Rajat Shinde and Elena Simperl and Goeffry Thomas and Slava Tykhonov and Joaquin Vanschoren and {Van Der Velde}, Jos and Steffen Vogler and Wu, {Carole Jean}",

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doi = "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, CJ 2024, 'Croissant: A Metadata Format for ML-Ready Datasets', Paper presented at 8th Workshop on Data Management for End-to-End Machine Learning, DEEM 2024, Santiago, Chile, 9/06/2024 pp. 1-6. https://doi.org/10.1145/3650203.3663326

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AU - Benjelloun, Omar

AU - Conforti, Costanza

AU - Gijsbers, Pieter

AU - Giner-Miguelez, Joan

AU - Jain, Nitisha

AU - Kuchnik, Michael

AU - Lhoest, Quentin

AU - Marcenac, Pierre

AU - Maskey, Manil

AU - Mattson, Peter

AU - Oala, Luis

AU - Ruyssen, Pierre

AU - Shinde, Rajat

AU - Simperl, Elena

AU - Thomas, Goeffry

AU - Tykhonov, Slava

AU - Vanschoren, Joaquin

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AU - Vogler, Steffen

AU - Wu, Carole Jean

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KW - reproducibility

KW - responsible AI

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Croissant: A Metadata Format for ML-Ready Datasets

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