Note:-I’m updating the notes(blog kinda) time to time as I deep-dive more about them..

Data Version Control (DVC) is an open-source tool that brings version control capabilities to machine learning projects, specifically for data and models. Think of it like Git, but designed for the large files common in ML, allowing you to track, reproduce, and manage your ML workflows.

Why DVC?

In machine learning, the models are only as good as the data they're trained on. Data changes, models evolve, and experiments are run frequently. DVC helps in:

• Tracking and saving ML models: Not just about the code, but the actual trained models and the data they were trained on.
• Creating and switching between different versions of data and ML models: Helps in switching to an older version of the dataset if needed.
• Comparing model metrics among experiments: it helps in understanding every experiment done at different iteration of the model

Getting Started with DVC (A Beginner's Guide)

Let's walk through a practical example.

1. Set Up Your Environment

First, create a clean space for your project.

• Create a virtual environment: Open your terminal or IDE and run:Bash

  python -m venv dvc_project_env

  This creates a folder named dvc_project_env containing your isolated Python environment.

• Activate the virtual environment:

  • On Windows: Bash

    .\\dvc_project_env\\Scripts\\activate

  • On macOS/Linux: Bash

    source dvc_project_env/bin/activate

• **Install DVC:**Bash

  pip install dvc

2. Initializing the Project and DVC

So,let’s deep-dive in this:-