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
Explore FastAPI in the interactive universe or train with live cohorts.
Want this FastAPI learning path turned into your own plan?
A roadmap shows the destination. In 1:1 mentorship I turn it into a week-by-week learning path for your background, review your projects, and prepare you for interviews until you land the role. Online, in your time zone.
Start with one 60-minute 1:1 call: .
Prefer a structured live course? Join the MLOps · AIOps · LLMOps · Agentic AI masterclass →