Data Versioning with DVC
Git cannot hold 500 GB of training data — but the same discipline still applies. DVC content-hashes your data and pipelines into tiny pointer files that Git tracks, keeps the bytes in S3/GCS remotes, and lets one Git commit pin the exact dataset+pipeline state a model was trained on. Covers the command workflow, where DVC fits versus warehouses/feature stores, and operational practice.
01.The Problem: Data Is a Mutable Global
Code sits in Git: every version pinned, diffable, checkable-out. Data does not.
So ML systems fail reproducibility audits in the quietest way:
- An ETL job overwrites a table partition in place.
- A scraped image disappears from the internet forever.
- Yesterday's "best model" cannot be retrained — or even re-evaluated — because its inputs no longer exist.
The requirements for a fix are exactly the things Git already gives code:
- Version datasets like code — named revisions, diff, checkout.
- Keep large binaries out of Git — object-storage remotes instead.
- Version the pipeline — datasets are derived; the transformations are code too, and must be pinned with their output.
- Preserve cache — re-running an unchanged stage should cost zero.
Can we get Git-like discipline for 500 GB without putting 500 GB in Git?
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How clear and actionable was this distributed systems breakdown?