Loading...
Loading...
Streaming that survives proxies, background jobs that survive redeploys, health checks that mean something, JSON logs with a trace id, a slim Docker image, and a shutdown sequence that does not drop in-flight work.
Message a mentor about fit, prerequisites, or where to start. Replies come on WhatsApp, usually within a day.
Engineers are learning here from
Take a FastAPI AI service from localhost to production. Stream tokens with SSE, run background jobs, lock down CORS, wire liveness and readiness probes, trace requests with JSON logs, ship a slim multi-stage Docker image, and drain work on SIGTERM.
Production FastAPI patterns for AI apps: SSE, jobs, CORS, probes, logs, Docker, graceful shutdown.
What you'll ship
What you'll learn
Curriculum
SSE streaming
Stream LLM tokens with Server-Sent Events, keep connections alive with heartbeats, and consume the stream from a client
Background job queue
Run async background work with idempotency keys, status polling, and failure recovery
CORS and security headers
Configure middleware for cross-origin requests and baseline security response headers
Liveness vs readiness
Expose two health endpoints that tell the orchestrator two very different things
Request-ID and JSON logs
Thread a trace id through every log record and emit structured JSON lines you can actually query
Multi-stage Dockerfile
Build on a slim base with uv, drop privileges, and ship a small runtime image
Graceful shutdown
Drain in-flight background jobs and close SSE streams cleanly when SIGTERM arrives
Who it's for
who have shipped FastAPI apps but have never owned one in production with real traffic
whose streaming endpoint works on a MacBook and breaks behind nginx
who need a reference implementation of the deployment concerns they audit on every AI service
FAQ
No. The patterns are covered with FastAPI and Docker Compose. Liveness and readiness probes map directly to Kubernetes, ECS, Nomad, or any orchestrator that supports HTTP health checks.
No. The workshop repo uses a small provider abstraction so you can run with OpenRouter, Fireworks, Gemini, or OpenAI. The deployment patterns are provider agnostic.
No. Everything runs on your laptop with Docker Desktop or Colima. The only paid piece is whichever LLM API key you already have.
Yes. SSE, background jobs, CORS, health probes, JSON logs, multi-stage Docker, and graceful shutdown apply to any async Python service. AI just makes the pain more obvious because tokens are slow and streams are long.
Pricing
Subscribe to Pro for every paid course, or buy just this one.
Unlock this course and every paid course plus workshop replays. One subscription.
You save 54% with regional pricing
One-time purchase. Lifetime access to every lesson, exercise, and update.
You save 47% with regional pricing
Still deciding? Ask Param a question
Deploying AI applications with FastAPI and Docker
$79 one-time