AI Assurance Directory

AI assurance & verification

Croissant (MLCommons)

Evidence strength: Emerging 0 cited · 2 analyst How this is scored

MLCommons metadata format and Apache-2.0 Python library (pip install mlcroissant; Python 3.10+) that describes ML datasets in four layers, plus a v1.0 RAI vocabulary (dataBiases, dataLimitations, dataUseCases). Discoverable via Google Dataset Search; loaders for TensorFlow, JAX and PyTorch.

Certification evidence

0 cited mappings · 2 analyst assessments across 2 frameworks. Counts are public. Requirement labels and quotes stay on Pro.

Analyst assessment: 1 EU AI Act article — see which with Pro

Analyst assessment: 1 NIST AI RMF function or subcategory — see which with Pro

Certification relevance. Searched vendor and publisher pages; no cited clause, function or article.

No cited mapping or literature quote yet. Analyst assessments, if any, stay on Pro.

Listing detail and source evidence (Pro)

Included on Pro, Team and Enterprise. Public summaries, tags and official links on this page stay free.

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Analyst assessments (Pro)

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Procurement evidence pack (Pro) — print cited mappings and analyst notes. The print view is not on this page.

Vendor / publisher
MLCommons
Type
Open source
Open source
Open source · Apache-2.0 on the official repository https://github.com/mlcommons/croissant. Badge is used only when that repo shows an OSI-approved license.
Official site
https://mlcommons.org/working-groups/data/croissant/
Industries
Frameworks named on cited source
None tagged. The cited pages do not name a framework we cover.
Last verified

Listing is not an endorsement, certification or recommendation. Capabilities, availability and framework support can change. Confirm with the vendor and the relevant authority. See the disclaimer.