Croissant

• Croissant is a metadata description format
• Ml datasets are a combination of structured and unstructured data, which make them complicated to manage
• Croissant was built on top of schema.org, and has more details relative to it
• The format has 4 layers
  • dataset level metadata
  • resource description
  • content structure
  • ml semantics
• Croissant does not require any changes to underlying data
• Analysis and visualization tools work out of the box for all datasets
• Using croissant, datasets can be exposed consistently throughout platforms
• Collaborations with google, huggingface, google dataset search also exist
• openml has deeper dataset description by default, slightly lesser in HF and kaggle
• once loaded, datasets can be imported elsewhere (torch, tf etc) easily
• Croissant editor - web app where you can use a GUI to enter the dataset descriptions
• NeurIPS also now recommends using the Croissant format