DVC
Git for data and models — version datasets and pipeline stages.
Maturity level: L2 — Can Build
Six perspectives on DVC
Roadmap
Learn with MLflow after Git — standard MLOps data hygiene.
Architecture
Data and pipeline versioning layer under experiment tracking.
Company
Seen in MLOps and ML platform job descriptions.
Projects
Never train on unversioned data again.
Interview
Reproducibility and data lineage.
Career
MLOps maturity signal.
What & Why
What: Data Version Control — tracks large files, datasets, and ML pipelines in Git repos.
Why: Reproducible ML requires versioned data, not just versioned code.
Build this
Version a training dataset and wire a reproducible DVC pipeline to MLflow.
Production reality
- ! Large remote sync times
- ! Cache corruption
- ! Pipeline drift
Interview preparation
- DVC vs MLflow — when to use each?
- How do you reproduce an old experiment?
Explore DVC in the interactive universe or train with live cohorts.
Want this DVC learning path turned into your own plan?
A roadmap shows the destination. In 1:1 mentorship I turn it into a week-by-week learning path for your background, review your projects, and prepare you for interviews until you land the role. Online, in your time zone.
Start with one 60-minute 1:1 call: .
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