Free 5-part course
MLOps for Data Scientists
From "it works in my notebook" to "it runs reliably in production," in five parts.
The 5-part practical course on MLOps for data scientists: project structure, serving with FastAPI + Docker, experiment tracking with MLflow, CI/CD, and production monitoring. Each part is a guide with a working code repo.
- 1
Notebooks Don't Ship: How to Structure Your ML Project So It Can Reach Production
Why notebooks stall at the prototype line, and the scripts-first structure, locked dependencies, and isolated environment (uv) that make a project reproducible anywhere.
Read Part 1 - 2
How to Actually Ship Your Model: FastAPI + Docker for Data Scientists
Wrap your model in a FastAPI service and package it with Docker so it runs the same on a server as on your laptop.
Read Part 2 - 3
Your Future Self Needs to Reproduce This: Experiment Tracking with MLflow
Track every run with MLflow and use the model registry to promote or roll back versions without touching your service code.
Read Part 3 - 4
Deployment Isn't a Moment, It's a Pipeline: Automating Your ML Rollouts
Build a CI/CD pipeline with GitHub Actions that tests, builds, and deploys automatically, even when a new model is promoted.
Read Part 4 - 5
How to Know If Your Deployed Model Is Actually Still Working
The four layers of monitoring, data-vs-model drift detection with Evidently, and how to retrain or roll back when quality slips.
Read Part 5