DVC (Data Version Control) - Machine Learning Tool

What is DVC?

DVC is an open-source tool that helps you manage datasets, models, and pipelines efficiently. It works alongside Git, enabling you to track changes in large files and data without cluttering your Git repo.

Key Features

• 🔁 Data & Model Versioning
  Track datasets and model files just like source code.

• ⚙️ ML Pipelines
  Define stages like data preprocessing, training, and evaluation using dvc.yaml. DVC automatically tracks dependencies and outputs.

• ☁️ Remote Storage Support
  Store large files in cloud storage (S3, GCS, Azure, etc.) while keeping your Git repo light.

• 📊 Experiment Tracking
  Run and compare experiments with different parameters or datasets.

• 🤝 Team Collaboration
  Share code and data across your team easily, without duplicating files.

Why Use DVC?

• Reproducible ML workflows

• Easy data and model versioning

• Simplified collaboration

• Scalable storage with cloud support

• Keeps your Git repo clean and lightweight

Conclusion
DVC bridges the gap between code versioning and data management in ML. It helps make your projects more organized, reproducible, and team-friendly. If you're working with data and Git, DVC is worth adding to your toolbox.