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Data Versioning with DVC: A Hands-On Guide
Git versions code but chokes on data. DVC fills the gap by keeping a small pointer to each dataset version in Git and the data itself in its own store. This hands-on guide versions a real dataset twice, rolls back to the first version, and builds a pipeline that reruns training only when something changes.
Model Versioning with MLflow and DVC
A model version is only useful if you can say what produced it. This guide combines DVC for data with MLflow for models, so each registered version records its data hash and Git commit, then rebuilds an old version from that record alone and checks that the predictions come out identical.