M11: Deployment & productionizing + Capstone
Your apps work, on your laptop, in your terminal. The last step of being an AI engineer is making them run for real: behind a web API anyone can call, packaged so they run the same anywhere, with your key kept safe and basic monitoring on. Then you build the thing that's yours: the capstone: design, build, and demo a complete AI app, and take a bow.
Today's win: your AI app runs as a real web service (FastAPI), packaged in a container (Docker), with your key passed safely and request latency/cost logged, then you scope your capstone.
Today you will
- Wrap an AI app in a FastAPI web API (
/health,/chat) and try it in the browser - Containerize it with Docker, passing your key safely at run time (callback to Course 01)
- Add basic monitoring (latency + token/cost logging), then kick off your capstone
Run of show (~70 min + capstone)
| Time | What we do |
|---|---|
| 0:00 | Hook + the win we're chasing |
| 0:05 | The one idea: a script → a service anyone can call (full read in notes.md) |
| 0:10 | Lab Part A: wrap the app in FastAPI; hit it from the browser |
| 0:35 | Lab Part B: containerize with Docker; run it; read the logs |
| 0:55 | Capstone kickoff: pick a track, scope it (see CAPSTONE.md) |
| - | Build your capstone, then demo it |
If you get stuck
- New installs:
fastapi+uvicorn(pip), and Docker (see the Docker guide). Short on time or blocked on Docker? You can finish on the FastAPI/uvicorn path alone, the lab has a no-Docker fallback. - Never bake your key into the image. Pass it at run time (
--env-file .env);.dockerignore+.gitignorekeep.envout. Re-read the You should now see line. - Capstone feeling big? It isn't a new skill, it's combining M4-M10. Start from the smallest version that runs, then add.
Optional challenge
Deploy your container somewhere others can reach it (a free host, or share your screen running it),
and have a classmate call your /chat endpoint. The moment someone else's request hits your AI
service is the moment you're an AI engineer.