🚀 Tutorial: Data and Model Versioning with DVC

🎓 What is this? This tutorial provides hands-on experience with Data Version Control (DVC), demonstrating how to manage multiple datasets and machine learning model artifacts alongside your code. We'll use a classic image classification problem (cats vs. dogs) to illustrate DVC's capabilities for reproducibility and collaboration in MLOps.

👩‍💻 Who is this for? ML Engineers, Data Scientists, and AI Developers who need to manage large datasets and models within Git repositories, ensuring experiment reproducibility and traceable deployments. A basic understanding of Python, Git, and command-line operations is assumed.

🎯 What will you learn?

• How to initialize a DVC project within a Git repository.

• How to version large files and directories using dvc add.

• How DVC tracks data changes using .dvc files and a cache.
• How to switch between different versions of your data and models using dvc checkout.
• How to set up and use remote storage to store and share DVC-tracked data with dvc push and dvc pull.
• Advanced data access methods like dvc get, dvc import, and dvc import-url.
• The fundamentals of DVC pipelines using dvc stage add and dvc repro for automating ML workflows.

📂 Project Structure

mlops-get-started-iris/
├── .github/                # GitHub specific configurations (e.g., Workflows for CI)
├── docs/
│   └── DEVELOPMENT.md      # Detailed guide for developers on coding standards and tools
├── .gitignore              # Global Git ignore patterns for the project
├── .pre-commit-config.yaml # Configuration for pre-commit hooks (Ruff, Mypy, etc.)
├── .python-version         # Specifies the preferred Python version (e.g., for pyenv or uv)
├── .secrets.baseline       # Baseline file for detect-secrets (prevents committing secrets)
├── Makefile                # Defines useful development commands (e.g., make lint, make test)
├── pyproject.toml          # Project configuration, dependencies, and tool settings (PEP 621)
└── uv.lock                 # Lock file for reproducible Python dependencies (generated by uv)

🛠️ Prerequisites

Ensure you have the following installed on your system:

• Python 3.11+: We recommend using the version specified in the .python-version file.
• uv: A fast Python package installer and project manager. See the uv installation guide.

🚀 Quick Start: Installation & Setup

1. Clone the Repository:

   git clone <YOUR_REPOSITORY_URL>
   cd dvc-1-get-started

2. Set Up Python Environment

   # Create a virtual environment (e.g., named .venv) using the project's Python version
   uv venv .venv --python 3.12
   
   # Activate the virtual environment:
   # On macOS and Linux:
   source .venv/bin/activate
   # On Windows (PowerShell):
   # .\.venv\Scripts\Activate.ps1
   # On Windows (Command Prompt):
   # .\.venv\Scripts\activate.bat
   

3. Install Dependencies: We'll use uv to create a virtual environment and install dependencies.

   # Install project dependencies, including development tools:
   uv sync --dev
   
   # Initialize Pre-commit Hooks
   uv run pre-commit install

   Alternatively, the Makefile provides a shortcut:

   make install # This target in the Makefile should execute the uv commands above

You're now ready to start!

▶️ Running the Tutorial

Open the tutorial.md file to start the tutorial.

💻 Development Workflow & Tools

For a deeper dive into the development workflow, tool configurations, and contribution guidelines, please consult the DEVELOPMENT.md file.

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Happy DVC journey! 🚀