From Jupyter Notebook to Production: A Practical MLOps Guide
The article outlines how to turn experimental Jupyter notebooks into reliable AI services by adopting MLOps practices such as reproducible pipelines, containerization, CI/CD, and monitoring for drift. It highlights common pitfalls—stateful notebooks, hidden dependencies—and provides concrete steps to build scalable, observable model serving infrastructure.