Project 6: End-to-End ML Pipeline

Build a complete, production-style ML system from data to a local/staging deploy (API + metrics stub + retraining plan). Not a blank check for hardened production.

Difficulty

Advanced

Time Estimate

2-3 weeks

Skills You'll Practice

• MLOps
• CI/CD
• Model Deployment
• Monitoring

Learning Objectives

• Build end-to-end ML pipeline
• Use data versioning (DVC)
• Track experiments (MLflow)
• Deploy models (Docker, Kubernetes)
• Set up monitoring

Project Components

• Data pipeline
• Training pipeline
• Model registry
• API service
• Monitoring dashboard
• Automated retraining

Resources

• MLflow Documentation
• DVC Documentation
• MLOps Guide