DVC Tutorial: Build Reproducible ML & Scientific Workflows

Most machine learning projects do not become messy all at once. They drift there.

A dataset gets cleaned twice. A notebook is copied and renamed. A model file is saved without the parameters that created it. A teammate reruns training and gets a different score. Someone asks which version of the data produced the result in last month’s report, and the answer is buried somewhere in Slack, local folders, or memory.

DVC, short for Data Version Control, is made for exactly this kind of work. It brings version control habits to datasets, models, metrics, parameters, and pipeline outputs, while still letting Git do what Git is good at: tracking code and small text files. DVC describes itself as an open-source tool for data management, ML pipeline automation, and experiment management, designed to work with Git, cloud storage, CI/CD tools, and ordinary command-line workflows.

The useful thing about DVC is that it does not ask you to move your whole project into a new platform. You keep your repository. You keep your scripts. You keep Python, R, Julia, Bash, or whatever tools your workflow already uses. DVC adds a layer of memory around the files and commands that produce your results.

That matters because reproducibility is not just a nice research habit. It is how you answer…