FastAPI Development
Build the Python APIs everything else calls — typed, async, documented and fast enough to sit in front of a model. Every model, agent and RAG pipeline eventually needs to be an API somebody else can call: this is that layer. Built for fresh IT graduates and working developers alike, with AI/ML serving as a first-class module.
FastAPI holds a position no other framework in our catalogue does: it is the default serving layer for Python AI and ML workloads, and the default choice for new Python APIs that aren't full Django applications. Every model, agent and RAG pipeline eventually needs to be an API somebody else can call — this is that layer. That's why AI/ML serving gets a first-class module here, not an afterthought: streaming token-by-token output, long-running inference and job handoff, backpressure against rate-limited providers, cost and token accounting at the API boundary, and exposing capabilities as tools for agents.
The technical theme most courses miss is
async correctness. The single most common
production failure in FastAPI applications is blocking the event
loop — a synchronous database driver, a sync HTTP client or a
blocking SDK call inside an async route. It works perfectly on a
laptop and destroys concurrency under load. This course teaches
when to write async def and when a regular
def route is the right answer, what the framework's
threadpool does behind the scenes, why async database sessions
are the only sane default for a relational database, and how to
detect a blocked event loop and offload CPU-bound work — plus
the everyday failure modes practitioners actually hit:
confusing validation errors, CORS misconfiguration, and
serialisation surprises.
It is designed for two audiences at once. If you are a fresh IT graduate targeting Python backend roles, this is the framework new Python APIs are built on. If you are a working professional — a Python developer shipping scripts or notebooks, a data scientist or ML engineer who needs to serve your own models, or a Django/Flask developer moving to an async, typed, API-first framework — this course turns what you know into production services. The program runs 8 weeks (~60 hours), live and build-along, closing with a containerised, monitored API capstone defended on architecture.
Where This Fits in Our Catalogue
- FastAPI Development (this course) — the engineering layer beneath our AI courses: the API in front of every model, agent and pipeline.
- Generative AI & LLM Engineering, AI Agent Development and MLOps Engineering mention FastAPI as their serving layer — this is the deep treatment of that layer.
- Node.js Development — same discipline, different ecosystem. The choice usually follows your team's language and surrounding stack; Python wins where the workload touches data science, ML or AI tooling.
Why Learn FastAPI When AI Scaffolds Endpoints?
Same honest answer as our React, Next.js and Node courses: AI scaffolds FastAPI endpoints convincingly and gets the async model wrong in ways that only appear under concurrency — a blocking call inside an async route looks fine locally and collapses at load. The value is architecture, review, debugging and operational judgment. You'll use AI tooling as an accelerator you review, and you'll learn the failure patterns generated code repeats — so you ship its speed without shipping its mistakes.
Course at a Glance
- Duration: 8 weeks | ~60 hours | live + hands-on
- Format: Live online · build-along · deployed services
- Prerequisite: Working Python. Type hints, async and web fundamentals taught from first principles. No prior API or backend experience required.
- Curriculum: 9 modules, from modern Python to deployment and capstone — with AI/ML serving as a first-class module
- Portfolio: 7 deployed, production-style services plus a defended capstone
- Curriculum reviewed: July 2026 — FastAPI, Pydantic and SQLAlchemy move fast; this syllabus tracks the current versions
Course Highlights
- Every module leads with what you build and ship, not what you "learn about"
- Async correctness as a headline theme — the blocking-call failure most tutorials never explain
- AI/ML serving as a first-class module: streaming, cancellation, backpressure, cost accounting
- Dependency injection taught as the framework's best architectural feature
- Async database layer by default — with the honest account of when SQLModel convenience becomes constraint
- Real failure modes named: validation confusion, CORS, serialisation surprises
- Observability from day one: structured logs, correlation IDs, tracing with OpenTelemetry
- AI tooling as an accelerator you review, never a substitute for understanding
Hands-on Projects & Portfolio
Async database access, migrations, layered structure and full test coverage.
Roles, scopes, refresh tokens and rate limiting.
Streaming responses, timeouts, cancellation, retries and per-request cost tracking.
Warm-up, batching and concurrency limits under load test.
Submit, poll, webhook callback — and a worker doing the actual work.
Find the blocking calls in a slow service and fix them, with before/after load-test evidence.
Structured logs, metrics and tracing on a deployed service.
Production API, containerised, monitored and defended on architecture.
Tools & Stack
Python FastAPI Pydantic SQLAlchemy SQLModel Alembic PostgreSQL Redis Celery or ARQ uv Uvicorn httpx pytest Testcontainers Ruff Docker Kubernetes OpenTelemetry nginx AWS GitHub Actions Git
Career Outcomes
FastAPI has become the default choice for new Python APIs outside full Django applications — a position strengthened by the fact that most AI and ML work happens in Python, which means the team that trains the model usually also ships the API in front of it. Python developers who can build and operate a production service, rather than only write scripts or notebooks, are disproportionately employable right now. This course targets:
- Backend Developer (Python)
- API Engineer
- Python Developer
- Full-Stack Developer
- ML/AI Platform Engineer
- MLOps Engineer
- Data Engineer building service layers
- Microservices Developer
Who This Course Is For
- Final-year students and fresh IT graduates targeting Python backend roles
- Python developers who write scripts, notebooks or automation and want to ship services
- Data scientists and ML engineers who need to serve their own models — a large and underserved audience
- Django and Flask developers moving to an async, typed, API-first framework
- Node, Java and .NET backend developers adding Python to their stack
- AI engineers building agent, RAG or LLM applications that need a real API layer
Material Includes
- Class recordings
- Service starter repos & API templates
- Async-review checklists & load-test harnesses
- Certificate
Prerequisites
- Working Python
- Type hints, async and web fundamentals taught from first principles
- No prior API or backend experience required
Estimated Time: 6 Hours
You ship:- A modern, linted, lockfiled project skeleton — typed throughout, structured to scale past one file — that every later service builds on
- Type hints as the foundation the framework is built on; the ASGI model and how it differs from WSGI
- async/await and the event loop; concurrency versus parallelism
- Project setup with a modern package manager and lockfiles; linting and formatting; a structure that scales
Estimated Time: 6 Hours
You ship:- A modular API with routers, response models and OpenAPI documentation good enough to hand to another team — plus deliberate startup logic via lifespan
- Routing, path and query parameters, request bodies, status codes and response models
- Routers for modular applications; application lifespan and startup logic
- The automatically generated OpenAPI documentation — and making it genuinely useful
Estimated Time: 6 Hours
You ship:- A validation layer with custom validators and clean schema separation — request vs response vs database — whose failures read as messages an API consumer can act on
- Models, field constraints and custom validators; nested and recursive models
- Serialisation and response shaping; separating request schemas from response schemas from database models
- Settings and configuration management; actionable validation errors
Estimated Time: 7 Hours
You ship:- A refactor that replaces globals and singletons with injected dependencies — sessions, auth, shared logic — making the same code testable overnight
- The dependency system as the framework's best architectural feature; dependencies for database sessions, authentication and shared logic
- Sub-dependencies and caching; scoped resources and cleanup; class-based dependencies
- Injection for testability instead of globals or singletons
Estimated Time: 7 Hours
You ship:- A production data layer on async sessions — modern ORM style, eager loading that kills N+1s, migrations, transactions — behind a repository pattern, with a written single-model vs separate-schema decision
- Async sessions and connection pooling — why the synchronous session silently negates the framework's main advantage
- The modern ORM style; relationships and eager loading; migrations; transactions; converting ORM objects into validated response models
- Repository patterns; the single-model approach and when its convenience becomes a constraint
Estimated Time: 7 Hours
You ship:- An OAuth2-secured service — token issuance, refresh and validation, roles and scopes, dependency-injected auth, rate limiting — hardened against the OWASP API risks with CORS done correctly
- OAuth2 flows; token issuance, validation and refresh; password hashing; API keys and service-to-service auth
- Role and scope based access; dependency-injected auth; CORS done correctly; rate limiting at application and gateway layers
- Input hardening; secrets management; the OWASP API risks
Estimated Time: 9 Hours
You ship:- An LLM gateway API — token-by-token streaming, timeouts and client cancellation, retries and circuit breaking, per-request cost tracking — plus a model-serving endpoint with warm-up, batching and concurrency limits proven under load test
- Streaming token-by-token responses; server-sent events; background tasks versus a real job queue for long-running inference; timeouts and client cancellation
- Concurrency limits and backpressure against rate-limited providers; retries and circuit breaking; caching and cost accounting; batching; model loading and warm-up
- File and multimodal uploads; exposing internal capabilities as tools for agents over emerging agent-integration protocols
Estimated Time: 6 Hours
You ship:- The async-correctness workshop: find the blocking calls in a slow service and fix them with before/after load-test evidence — on a service fully tested and traced end-to-end
- The test client and async testing; pytest fixtures and factories; database testing with containers; mocking external providers; contract testing
- Structured logging and correlation IDs; metrics and distributed tracing with OpenTelemetry
- Profiling; load testing; diagnosing a blocked event loop with evidence
Estimated Time: 6 Hours
You ship:- Your capstone: one production-grade API — small container image, health checks, graceful shutdown, horizontally scaled, CI/CD-deployed — monitored and defended on architecture
- ASGI servers, worker models and process management; container images that stay small; health checks and graceful shutdown; reverse proxies
- Environment configuration; horizontal scaling; serverless and its cold-start trade-offs; CI/CD
- Deployment targets compared honestly; capstone design, build, deploy and defense
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