Croissant: A Metadata Format for ML-Ready Datasets
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
2024 Spotlight Poster
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.
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