학술논문
Croissant: A Metadata Format for ML-Ready Datasets
Document Type
Working Paper
Author
Akhtar, Mubashara; Benjelloun, Omar; Conforti, Costanza; Gijsbers, Pieter; Giner-Miguelez, Joan; Jain, Nitisha; Kuchnik, Michael; Lhoest, Quentin; Marcenac, Pierre; Maskey, Manil; Mattson, Peter; Oala, Luis; Ruyssen, Pierre; Shinde, Rajat; Simperl, Elena; Thomas, Goeffry; Tykhonov, Slava; Vanschoren, Joaquin; van der Velde, Jos; Vogler, Steffen; Wu, Carole-Jean
Source
Subject
Language
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.
Comment: Published in Proceedings of ACM SIGMOD/PODS'24 Data Management for End-to-End Machine Learning (DEEM) Workshop https://dl.acm.org/doi/10.1145/3650203.3663326
Comment: Published in Proceedings of ACM SIGMOD/PODS'24 Data Management for End-to-End Machine Learning (DEEM) Workshop https://dl.acm.org/doi/10.1145/3650203.3663326