[2403.19546] Croissant: A Metadata Format for ML-Ready Datasets
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Computer Science > Machine Learning

arXiv:2403.19546 (cs)
[Submitted on 28 Mar 2024 (v1), last revised 9 Dec 2024 (this version, v3)]

Title:Croissant: A Metadata Format for ML-Ready Datasets

Authors:Mubashara Akhtar, Omar Benjelloun, Costanza Conforti, Luca Foschini, Joan Giner-Miguelez, Pieter Gijsbers, Sujata Goswami, Nitisha Jain, Michalis Karamousadakis, Michael Kuchnik, Satyapriya Krishna, Sylvain Lesage, Quentin Lhoest, Pierre Marcenac, Manil Maskey, Peter Mattson, Luis Oala, Hamidah Oderinwale, Pierre Ruyssen, Tim Santos, Rajat Shinde, Elena Simperl, Arjun Suresh, Goeffry Thomas, Slava Tykhonov, Joaquin Vanschoren, Susheel Varma, Jos van der Velde, Steffen Vogler, Carole-Jean Wu, Luyao Zhang
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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 creates a shared representation across ML tools, frameworks, and platforms. Croissant makes datasets more discoverable, portable, and interoperable, thereby addressing significant challenges in ML data management. Croissant is already supported by several popular dataset repositories, spanning hundreds of thousands of datasets, enabling easy loading into the most commonly-used ML frameworks, regardless of where the data is stored. Our initial evaluation by human raters shows that Croissant metadata is readable, understandable, complete, yet concise.
Comments: Published at the NeurIPS 2024 Datasets and Benchmark Track. A shorter version appeared earlier in Proceedings of ACM SIGMOD/PODS'24 Data Management for End-to-End Machine Learning (DEEM) Workshop this https URL
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Databases (cs.DB); Information Retrieval (cs.IR)
Cite as: arXiv:2403.19546 [cs.LG]
  (or arXiv:2403.19546v3 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2403.19546
Related DOI: https://doi.org/10.1145/3650203.3663326

Submission history

From: Luis Oala [view email]
[v1] Thu, 28 Mar 2024 16:27:26 UTC (1,268 KB)
[v2] Thu, 30 May 2024 16:20:04 UTC (1,276 KB)
[v3] Mon, 9 Dec 2024 18:37:55 UTC (1,728 KB)
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