Croissant: A Metadata Format for ML-Ready Datasets
/ Authors
Mubashara Akhtar, Omar Benjelloun, Costanza Conforti, Joan Giner-Miguelez, Nitisha Jain, Michael Kuchnik, Quentin Lhoest, Pierre Marcenac, M. Maskey, Peter Mattson
and 9 more authors
Luis Oala, Pierre Ruyssen, Rajat Shinde, E. Simperl, Goeffry Thomas, Slava Tykhonov, J. Vanschoren, Steffen Vogler, Carole-Jean Wu
/ 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.
Journal: Proceedings of the Eighth Workshop on Data Management for End-to-End Machine Learning