Data Version Control with DVC

Set up DVC for data and model version control.

Project requirements:
- Data size: [TOTAL DATA SIZE]
- Storage backend: [S3/GCS/AZURE/LOCAL]
- Team size: [NUMBER OF COLLABORATORS]
- CI/CD: [YES/NO]

DVC setup requirements:
1. Initial configuration:
   - Repository setup
   - Remote storage configuration
   - Git integration
2. Data tracking:
   - Large file tracking
   - Directory tracking
   - External data
3. Pipeline definition:
   - Stage dependencies
   - Parameter management
   - Metrics tracking
4. Experiment management:
   - Experiment branches
   - Comparison tools
   - Metrics visualization
5. Collaboration:
   - Data sharing
   - Access control
   - Conflict resolution
6. CI/CD integration:
   - Automated pipelines
   - Model registry
   - Deployment triggers
7. Best practices:
   - .dvcignore patterns
   - Cache management
   - Storage optimization

Integrate with existing Git workflow.