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DVC
Git-like data and model versioning with pipelines and local experiment tracking
Apache-2.0
ABOUT
Git handles code well but breaks down when teams need to version large datasets, model artifacts, and multi-step preprocessing or training pipelines. DVC keeps lightweight metadata in Git while storing heavy files in external remotes, making data changes, pipeline stages, metrics, and experiment results reproducible and shareable without stuffing binaries into the repository.
INSTALL
pip install dvcINTEGRATION GUIDE
1. Version training datasets and model artifacts without storing binaries in Git
2. Define reproducible multi-step ML pipelines in dvc.yaml
3. Cache pipeline outputs so only changed stages rerun
4. Store data in S3, GCS, Azure, SSH, or other remote backends
5. Compare experiment metrics, params, and plots across local runs
6. Share and reproduce experiments across teammates using Git and DVC remotes
TAGS
data-versioningmlopspipelinesexperiment-trackingreproducibilitygitcli