FastAPI

Production Python APIs for ML inference, RAG endpoints, and agent backends.

Maturity level: L2 — Can Build

Six perspectives on FastAPI

Roadmap

Learn after Python — before shipping any model or RAG feature.

Architecture

HTTP layer in front of models, retrievers, and agent orchestrators.

Company

Very common in AI engineer and GenAI application postings.

Projects

Expose every model and agent as a documented API.

Interview

API design, async patterns, and production hardening.

Career

Turns notebook code into something a team can deploy.

What & Why

What: Modern async Python web framework for building high-performance APIs.

Why: AI Engineer and LLMOps roles expect you to ship REST APIs around models — FastAPI is the default.

Build this

FastAPI service wrapping an LLM with structured outputs, health checks, and request logging.

Production reality

  • ! Blocking calls in async routes
  • ! Memory growth under load
  • ! Auth gaps
  • ! Cold start latency

Interview preparation

  • Why FastAPI for ML services?
  • Sync vs async for inference endpoints
  • API design for agents

Connected skills

KubernetesDockerPythonLLMsRedis

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