Free 5-Part Practical Course for Data Scientists
MLOps for Data Scientists
Learn what it takes to go from a notebook to a monitored system running in production. Each part is a practical guide, a working code repo, and a short video.
Delivered to your inbox, one part a day.
What the five parts cover
You can train a model. The gap is everything between "it works in my notebook" and "it runs reliably in production." Each part closes one piece of that gap, and backs it with a full guide, a working code repo, and a short video. Nothing held back.
- 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.
- 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.
- 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.
- 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.
- 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.
Who this is for
- You can train a solid model, but you freeze the moment someone says "now ship it."
- You've never taken anything past a notebook into something another system can actually call.
- You keep hearing "MLOps," "Docker," "CI/CD," and "drift," and want to see how they actually fit together, with the code to match.
- You want the part of the job that makes you harder to replace: owning the model past the deploy step.
Who's teaching this
Andres Vourakis
Senior Data Scientist with 8+ years in tech and applied AI/ML. For most of my career, the hard part was never the modeling. It was everything after: getting a model out of my notebook and into production where people could actually use it. I learned that side the hard way, on real systems at real companies, structuring projects, wrapping models in services, containerizing them, automating deployments, and keeping an eye on them once they were live. Since then I've shipped ML and AI systems to production. This free course is the map I wish someone had handed me when I started.
Ready to start closing the gap?
Go from "I trained a model" to "I operate a system in production."