Data Version Control Overview

Introduction

This project uses DVC (Data Version Control) for managing datasets and ML models. DVC is an open-source version control system for machine learning projects that works on top of Git.

Why DVC?

DVC provides several key benefits for ML projects:

1. Version Control for Data
2. Track large files without storing them in Git
3. Version datasets and models like code

4. Share data and models with team members

5. Reproducible Experiments

6. Track dependencies between data, code, and models
7. Reproduce experiments with exact data versions

8. Compare experiment results

9. Pipeline Management

10. Define ML pipelines as code
11. Automate data processing and model training

12. Track pipeline dependencies

13. Storage Integration

14. Support for various storage backends
15. Efficient data transfer
16. Team collaboration

Project Setup

Our DVC configuration includes:

1. Data Storage

   data/
   ├── raw/          # Original, immutable data
   ├── interim/      # Intermediate data
   └── processed/    # Final, cleaned data
   

2. Model Storage

   models/
   ├── trained/     # Trained model files
   └── evaluation/  # Model evaluation results
   

3. Pipeline Stages

   dvc.yaml         # Pipeline definition
   params.yaml      # Model parameters
   

Best Practices

1. Data Organization
2. Keep raw data immutable
3. Document data sources and versions

4. Use clear naming conventions

5. Model Management

6. Version all model artifacts
7. Track model parameters

8. Store evaluation metrics

9. Pipeline Structure

10. Create modular pipeline stages
11. Define clear dependencies
12. Document pipeline parameters