Getting Started with DVC (Data Version Control)

•Anil Verma
DVCMachine Learning

Introduction to DVC (Data Version Control)

Modern machine learning and data science projects suffer from a common problem: code is versioned, but data and models are not. This is where DVC (Data Version Control) comes in.

DVC is an open-source tool that brings Git-like versioning to large datasets, machine learning models, and experiments—without storing heavy files directly in Git.

In this blog, we’ll explore:

  • What DVC is
  • Why it is needed
  • Core concepts
  • A simple workflow
  • How DVC fits into MLOps

What is DVC?

DVC (Data Version Control) is a command-line tool that helps track data, models, and ML pipelines. It works alongside Git and stores metadata in Git while pushing large files to external storage such as:

  • Amazon S3
  • Google Cloud Storage
  • Azure Blob Storage
  • Local or network storage

Think of DVC as Git for data.

Why Do We Need DVC?

Traditional version control systems like Git are not designed to handle large binary files efficiently. In ML projects, this leads to:

  • Bloated repositories
  • No reproducibility
  • Confusing experiment tracking

DVC solves these issues by:

  • Keeping Git repositories lightweight
  • Making experiments reproducible
  • Tracking data and models efficiently
  • Enabling collaboration between data scientists

Core Concepts of DVC

1. Data Tracking

Instead of committing large files to Git, DVC tracks them using .dvc files:

dvc add data/raw-data.csv

This generates a .dvc file that Git can track, while the actual data is stored remotely.

2. Remote Storage

DVC uses remotes to store data and models:

dvc remote add -d storage s3://my-dvc-bucket

This keeps your Git repository clean and fast.

3. Pipelines

DVC allows you to define ML pipelines with clear dependencies and outputs:

dvc stage add -n train \
  -d train.py -d data/processed \
  -o model.pkl \
  python train.py

With pipelines, you can reproduce the entire workflow using:

dvc repro

4. Experiment Tracking

DVC tracks parameters, metrics, and models across experiments:

dvc exp run
dvc exp show

This makes comparing experiments easy and transparent.

Typical DVC Workflow

  1. Initialize Git and DVC
  2. Add data using dvc add
  3. Push data to remote storage
  4. Commit metadata to Git
  5. Run experiments and track metrics
  6. Reproduce results anytime
git init
dvc init

DVC in MLOps

DVC plays a critical role in MLOps by enabling:

  • Reproducible ML pipelines
  • Model versioning
  • CI/CD for ML
  • Collaboration across teams

It integrates well with tools like:

  • GitHub Actions
  • MLflow
  • Kubernetes
  • Cloud platforms

Advantages of Using DVC

  • Git-friendly data versioning
  • Storage-agnostic
  • Reproducible pipelines
  • Scales with team size
  • Open-source and extensible

When Should You Use DVC?

DVC is ideal if:

  • You work with large datasets
  • You need experiment reproducibility
  • You collaborate in ML teams
  • You want Git-based workflows for ML

Conclusion

DVC bridges the gap between software engineering and data science. By versioning data, models, and pipelines alongside code, it brings discipline, reproducibility, and collaboration to machine learning projects.

If you are serious about ML or data science, DVC is a tool worth adopting.

Happy Learning! 🚀

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