Data Versioning Pipeline

A production-ready MLOps pipeline for complete data and model lifecycle management using DVC, MLflow, and automated CI/CD.

🚀 Features

• Data Versioning: DVC-powered data and model versioning
• Experiment Tracking: MLflow integration for tracking experiments
• Model Registry: Centralized model version management
• Automated Pipeline: Reproducible ML pipeline with single command
• CI/CD: GitHub Actions for automated testing and validation
• API Deployment: FastAPI endpoint for model serving

📁 Project Structure

data-versioning-pipeline/
├── data/                  # Data directory
│   ├── raw/              # Raw datasets
│   └── processed/        # Processed datasets
├── models/               # Trained models
├── notebooks/            # Jupyter notebooks
├── src/                  # Source code
│   ├── data/            # Data processing modules
│   ├── models/          # Model training/evaluation
│   ├── utils/           # Utility functions
│   └── api/             # FastAPI application
├── tests/               # Unit and integration tests
├── configs/             # Configuration files
└── README.md

🔧 Setup

Instructions coming soon...

📊 Usage

Instructions coming soon...

👤 Author

Harshith

• GitHub: @harshithluc073
• Email: [email protected]

📝 License

MIT License