🚢 ML in Production — MLOps

One model, taken from notebook to production: reproducible training, MLflow tracking & registry, Docker, FastAPI, vLLM, CI/CD gates, drift monitoring, continuous retraining.

10-day mini-course — One model, taken from notebook to production: reproducible training, MLflow tracking & registry, Docker, FastAPI, vLLM, CI/CD gates, drift monitoring, continuous retraining.

Like every course on this site, each day is a deep, code-first lesson: an intuition-first explainer, a staged code walkthrough explaining the methodology line by line, visuals, and a 🧪 Your task exercise with a hidden solution. Theory lives in the AI & ML Encyclopedia; here you build.

▶ Start Day 1   📚 All mini-courses

Syllabus

Day	Lesson
Day 1	The Deployment Gap & the Plan
Day 2	Reproducible Training: Seeds, Pins, and Config-as-Code
Day 3	Experiment Tracking with MLflow
Day 4	The Model Registry: Versioning and Promotion by Alias
Day 5	Packaging the model: from pickle to a production Docker image
Day 6	Serving with FastAPI: a Typed Prediction API
Day 7	Serving LLMs: vLLM in Practice
Day 8	CI/CD for ML: Gates Before Glory
Day 9	Monitoring in Production: Latency, Drift, and Knowing Before Your Users Do
Day 10	Continuous Training: Closing the Loop