Turn throw‑away scripts into a reproducible pipeline you can trust, rerun, and share.
Why DVC for science?
Most scientific projects begin as a handful of notebooks and scripts. Then the data grows, colleagues want to reproduce your results, and your one‑off code becomes a workflow. DVC (Data Version Control) gives you a Git‑like way to version big data and models, describe your workflow as a pipeline, and reproduce any result on demand — without stuffing gigabytes into your Git history.
Below is a magazine‑style, end‑to‑end playbook that distills best practices from a classic tutorial by Déborah Mesquita (TDS Archive), DVC’s official quickstart, and a concise “five steps” guide — updated to reflect the modern dvc stage add workflow.
What you’ll build
A compact, four‑stage pipeline — prepare → featurize → train → evaluate — that versions your data and models, parameterizes experiments, logs metrics and plots, and travels cleanly with Git. This mirrors what most scientific workflows need: deterministic steps, tracked inputs/outputs, and easy re‑runs.
Prerequisites
- Git, Python 3.x, and DVC installed.
- A Git repository to hold your code and…
