Problem Statement The Iris dataset is a teaching staple, but nobody builds it like it would actually run in production. The goal was to demonstrate what a fully operationalised ML system looks like — CI/CD, drift detection, hot-swap model updates, explainability — using a problem simple enough to keep the focus on the infrastructure, not the data science.

Solution & Approach Implemented CI/CD/CT/CM (Continuous Training and Monitoring) using GitHub Actions. KS tests and PSI detect distribution drift on incoming data; a new model is auto-trained and hot-swapped via a rolling update if drift is confirmed. Every prediction returns a SHAP explanation. The test suite covers 115 tests at 84% coverage across FastAPI endpoints, ML pipeline stages, and data validation logic. Decoupled React frontend and FastAPI backend are deployed independently on Vercel and Google Cloud Run.

Key Highlights - Full CI/CD/CT/CM pipeline with GitHub Actions orchestration - KS test + PSI drift detection with automated hot-swap model updates - SHAP explanation returned with every prediction - 115 tests at 84% coverage across API, pipeline, and data validation layers - Zero-downtime rolling updates on model promotion Try the live app : https://iris-classification-system-ml-ops.vercel.app/