A demonstration of Data Version Control (DVC) for managing ML pipelines and data versioning.

DVC is an open-source version control system for machine learning projects. It helps you:

• Version control large files, data sets, machine learning models, and metrics
• Track ML experiments
• Create reproducible ML pipelines
• Collaborate with team members

. ├── data/ # Raw and processed data files │ └── raw.dvc # DVC file for raw data ├── src/ # Source code for data processing and model training ├── config/ # Configuration files ├── .dvc/ # DVC internal files ├── dvc.yaml # DVC pipeline definition ├── dvc.lock # DVC lock file for reproducible pipelines └── .dvcignore # Files/directories to be ignored by DVC 

1. Install project dependencies using uv:

uv sync dvc

2. Pull the data from remote storage:

dvc pull

3. Run the pipeline to reproduce all stages:

dvc repro

• Track data files: dvc add <file>
• Push data to remote storage: dvc push
• Pull data from remote storage: dvc pull
• Check status: dvc status