Description
Datasets, models, and experiment artifacts can be versioned alongside code for data science projects. It is useful for teams that need reproducible ML workflows and large-file tracking.
Data versioning can move private datasets to remote storage. Review remotes, credentials, access rules, and ignored files before pushing data.