I built a production ML inference API with FastAPI, Celery and Docker — here's the full architecture

Para 1 — The problem

"Most ML tutorials end at model.fit(). Getting a model into production is a completely

different skill. Here's how I built a real async

inference microservice."

Para 2 — Architecture diagram

Paste the ASCII diagram from your ARCHITECTURE.md

Para 3 — The three components

FastAPI handles HTTP (why async matters)

Celery handles background work (why not just threads)

Redis handles both queue and results (why one service)

Para 4 — Key code snippet (predict_async endpoint)

Show 15 lines of code — the async endpoint that

dispatches to Celery and returns task_id immediately

Para 5 — Testing strategy

"I used in-memory Celery eager mode so tests

run without Redis. Here's the conftest pattern."

Show 10 lines of conftest.py

Para 6 — The result

Screenshot of the UI dashboard

Screenshot of 47 tests passing

Closing line:

"If you want the full source code with Docker,

CI pipeline, Postman collection and deployment

guide, I packaged it here: [Gumroad link]"