End-to-End MLOps Pipeline for Fraud Detection

A production MLOps pipeline that trains, versions, deploys, and monitors a fraud-detection model with automated retraining.

Most machine learning projects stop at a trained model in a notebook, yet real systems fail because models are never versioned, deployed safely or monitored after release. Fraud detection makes this obvious: fraud patterns change quickly, so a model that was accurate last quarter quietly degrades. This project builds the full lifecycle around a fraud-detection model instead of only the model itself.

A feature pipeline prepares transaction data and stores reusable features. Training runs are tracked with MLflow, which records parameters, metrics and artifacts and registers each approved model version. Airflow orchestrates the workflow, and a CI/CD pipeline tests the code and the model before promoting it. The chosen model is packaged in Docker and served through a FastAPI endpoint that scores transactions in real time. Monitoring compares live data with the training data to detect drift, and when drift or falling accuracy is detected an automated retraining job is triggered, evaluated against the current model and promoted only if it performs better, with a rollback path if it does not.

You will learn experiment tracking, model registries, workflow orchestration, containerized serving, drift detection and safe promotion of models. The Project Reference Guide documents each stage with diagrams and trade-offs, and the Reference Implementation provides the complete pipeline with instructions to run it locally.