Setting up DVC for data and model versioning
DVC (Data Version Control) is a Git-compatible tool that adds management of large files to standard version control: datasets weighing hundreds of gigabytes, trained models, and experiment artifacts. Without it, teams store data in shared folders without revision history, lose track of code and specific dataset versions, and can't reproduce experiments from three months ago.
What is being configured?
A typical installation takes 1-2 days and includes:
- initialize DVC in an existing Git repository (
dvc init) - Configure remote storage: S3, GCS, Azure Blob, SSH, or local NFS
- creation of
.dvcfiles for tracking datasets and models - configuring
.dvcignoresimilar to.gitignore - cache configuration to speed up repeated operations
Remote storage example for S3:
dvc remote add -d myremote s3://mybucket/dvcstore
dvc remote modify myremote endpointurl https://...
Pipelines and reproducibility
DVC allows you to describe ML pipelines in dvc.yaml , where each stage (data preparation, training, evaluation) is linked to specific dependencies and output files. When input data changes, DVC automatically determines which stages need to be rerun.
stages:
train:
cmd: python train.py
deps:
- data/processed
- src/train.py
params:
- params.yaml:
- lr
- epochs
outs:
- models/model.pkl
metrics:
- metrics.json
Integration with MLflow and CI/CD
DVC works well with MLflow: DVC versions artifacts, and MLflow versions metrics and parameters. In CI/CD (GitHub Actions, GitLab CI), a dvc pull step is added to load data and a dvc repro step is added to reproduce the pipeline.
A typical implementation outcome: a team of 5 ML engineers goes from "what data was used for this model is unclear" to full reproducibility of any experiment over the past 6 months.