Croissant: A Metadata Format for ML-Ready Datasets

Mubashara Akthar, Omar Benjelloun, Costanza Conforti, 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, Vyacheslav Tykhonov, Joaquin Vanschoren, Steffen Vogler, Carole-Jean Wu

Research output: Working paper/discussion paperPreprint


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 languageEnglish
Publication statusPublished - 28 Mar 2024


  • machine learning
  • artificial intelligence


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