What is DVC?
Data Version Control (DVC) is an open-source version control system designed specifically for data science and machine learning projects. It ensures data integrity and reproducibility by tracking changes, managing dependencies, and automating workflows. DVC enables teams to collaborate effectively while maintaining a history of data transformations.
How to use DVC?
Initialize a DVC repository in your project directory. Track data files using 'dvc add' commands to create versioned links. Commit changes to Git while DVC manages large data files remotely. Use 'dvc repro' to reproduce experiments and 'dvc push/pull' to sync data across environments.
Core features of DVC?
- Data and code versioning with Git-like commands
- Seamless integration with popular ML tools and frameworks
- Efficient handling of large datasets through remote storage
- Pipeline automation for reproducible machine learning workflows
- Collaborative features with shared project configurations

