Model Online / On-Site
Duration
Modules 5 modules

Course Objective & Scope

FastAPI is the go-to framework in Python for ML and LLM services with custom business logic, yet common pitfalls—like blocking the event loop with CPU/GPU-bound inference inside async def or triggering OOM crashes when multiple workers duplicate models in memory—routinely break production systems. This course focuses specifically on architectural patterns for model serving with FastAPI.

Participants implement safe model loading and warmup via lifespan context managers, tensor/feature validation with Pydantic v2, Server-Sent Events (SSE) token streaming for LLMs, non-blocking execution via thread/process pools and OpenTelemetry/Prometheus instrumentation. Teams leave with a production-ready FastAPI inference service template.

Who Should Attend?

  • ML and AI engineers exposing models alongside custom business rules
  • Backend developers building LLM-powered applications and RAG services
  • Data scientists turning notebook models into production microservices
  • Prerequisite: intermediate Python and basic HTTP/REST knowledge

Key Takeaways

  • Load and warm up models once at startup using lifespan while managing GPU/CPU memory safely
  • Define strict input/output contracts and clear validation errors with Pydantic v2
  • Run CPU/GPU-bound model.predict() calls without blocking the asyncio event loop
  • Build LLM token-streaming endpoints with StreamingResponse/SSE and handle file/image uploads
  • Tune Uvicorn/Gunicorn workers, health checks and Prometheus metrics for production

Curriculum

Model Lifecycle and Input/Output Contracts
  • Loading models, GPU warmup and graceful resource cleanup with lifespan
  • Feature validation, batch request schemas and version metadata using Pydantic v2
Event Loop, Concurrency and Non-Blocking Inference
  • def vs async def: how CPU/GPU-bound model calls stall the event loop
  • run_in_threadpool, ProcessPoolExecutor and lightweight in-app micro-batching patterns
Multimodal Payloads and LLM Token Streaming
  • Processing image/audio uploads via UploadFile without memory spikes
  • Real-time LLM token streaming using StreamingResponse and Server-Sent Events (SSE)
Observability, Testing and Error Handling
  • Separating liveness probes from model-loaded readiness probes
  • Prometheus inference metrics and automated tests with pytest and TestClient
Production Server (Uvicorn/Gunicorn) and Container Deployment
  • Worker count versus GPU/RAM memory multiplication and --preload pitfalls
  • Multi-stage Dockerfiles, security middlewares and load-test verification

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