1 · Web foundations
Why a model needs a front end, the HTTP request/response round-trip, and your first Flask app.
●Track 2 · live
Flask & Streamlit
Wrap a trained model in a tiny web app or dashboard.
Two minimal web frameworks for ML practitioners: Flask for REST APIs that serve predictions, Streamlit for instant dashboards. Read-along code with animated diagrams of the request/response flow — execution lives outside the browser since these are server frameworks.
12 / 12 lessons5 chapters
2 · Flask for ML
Serve predictions over HTTP — a JSON API, an HTML form with Jinja templates, and robust input validation.
3 · Streamlit dashboards
Build interactive model apps with almost no frontend code — widgets, caching, layout, charts, and file uploads.
4 · Choosing & shipping
Flask vs Streamlit — when to use each — and how to containerize and deploy either one.
5 · Capstone
Expose one trained model two ways — a Flask JSON API and a Streamlit dashboard — end to end.