A comprehensive hands-on guide to building a local end-to-end ML platform for fraud detection. Starts by exposing the pitfalls of a naive ML approach (no experiment tracking, no model versioning, no data validation, no monitoring, no CI/CD), then incrementally adds MLflow for experiment tracking and model registry, Feast as a
Table of contents
Table of ContentsProject Overview and Setup1. Build a Simple Model and API (The Naive Approach)2. Where the Naive Approach Breaks3. Add Experiment Tracking and Model Registry with MLflow4. Ensure Feature Consistency with Feast5. Add Data Validation with Great Expectations6. Monitor Model Performance and Data Drift7. Automate Testing and Deployment with CI/CD8. Incident Response Playbook9. How to Put It All Together10. What's Next: Scale to ProductionConclusionGet the Complete CodeReferencesSort: