DVC
Data version control for ML: version datasets and models with Git, cloud storage, and reproducible pipelines.
Why it is included
Fills the gap between Git and terabyte-scale artifacts—essential for serious reproducible ML outside notebook-only workflows.
Best for
Teams sharing data + model lineage across researchers and CI without copying giant blobs into Git.
Strengths
- Git-native mental model
- Remote storage
- Pipelines
Limitations
- Requires discipline on remote cache layout and access control
Good alternatives
LakeFS · Git LFS alone · MLflow artifacts
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