About Data Version Control
Data Version Control (DVC) helps version data and machine-learning models alongside the code in an ML project. It is intended to make datasets and model artifacts shareable and reproducible, addressing the fact that large data files and trained models often need their own versioning workflow in addition to source-code history. By tracking data and models as part of a project process, DVC can help collaborators work from consistent inputs and reproduce results. Its role is supporting versioning and sharing for machine-learning assets rather than replacing the code version-control system. The supplied information does not describe storage backends, pipeline features, or model-tracking interfaces, so those details are not assumed here.
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