Croissant: A Metadata Format for ML-Ready Datasets
Mubashara Akhtar 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 Slava Tykhonov Joaquin Vanschoren Steffen Vogler Carole-Jean Wu
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.