Developer Publishes Guide to Building a Full Production-Grade MLOps Pipeline

A software developer has published a detailed walkthrough on building a complete, production-grade MLOps pipeline using the Titanic survival dataset as a working example. The guide covers the full lifecycle beyond model training, including data versioning with DVC, experiment tracking via MLflow, and automated testing with Pytest. The pipeline also incorporates containerization through Docker, CI/CD via GitHub Actions, and a FastAPI-based inference service. Monitoring is handled by Prometheus, while Evidently AI is used for data drift detection. The project, available on GitHub, aims to demonstrate the engineering practices that separate a notebook experiment from a deployable machine learning system.