Croissant: A Metadata Format for ML-Ready Datasets
This addresses data management challenges for ML practitioners by providing a standardized format, though it is incremental as it builds on existing metadata practices.
The paper tackles the problem of friction in working with machine learning data by introducing Croissant, a metadata format that creates a shared representation across ML tools, making datasets more discoverable, portable, and interoperable, with initial evaluation showing it is readable, understandable, complete, and concise.
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.