MLOps in practice: ship and monitor machine learning models

A model in a notebook is an experiment. A model that makes predictions for real users, keeps working as data changes and can be retrained safely is a product. The gap between the two is where many machine learning projects stall. MLOps is the set of practices that closes that gap.

This course takes a single, realistic model, a demand forecast for a chain of bakeries, and walks it through the whole lifecycle. We keep the tooling deliberately modest and widely used, so the habits transfer to any platform your team adopts later.

What you will learn

• Turning notebook code into a tested, reproducible training pipeline
• Versioning code, data and models
• Tracking experiments and registering models with MLflow
• Packaging a model behind a FastAPI service in a Docker container
• Batch versus online prediction, and choosing between them
• CI pipelines that test data, code and model quality before release
• Monitoring predictions, data drift and business outcomes
• Retraining strategies and safe rollouts with shadow and canary releases

Philosophy

Most production problems are not exotic. They are a column that changed type upstream, a feature computed differently in training and serving, or a model nobody noticed had degraded. We focus on simple, robust checks that catch those problems early. Tools are introduced only when a problem calls for them.

You should be comfortable with Python, pandas and scikit-learn, and know the basics of Git and the command line. Docker experience helps but is introduced from the start.