FastAPI From First Principles — Free Course
FastAPI From First Principles — Free Course
From Hello World to Kubernetes. Here’s everything I wish someone had told me.
I’ve been building APIs with FastAPI for over four years now — production systems handling millions of requests, authentication flows that actually work, database patterns that don’t fall apart at scale.
And I’ve made every mistake possible along the way. And don’t need to repeat the same.
So I wrote the guide I wish I had when I started: FastAPI from first principles to production deployment. No fluff. Real patterns from real production systems.
Who Is This For?
This series is for developers who are done with tutorials that stop at Hello World.
You might be:
- A backend engineer evaluating FastAPI for your next project
- A data scientist building APIs around ML models
- A senior developer who learns best by understanding why, not just how
- Someone tired of stitching together 47 Medium articles to get a complete picture
If you want to actually understand FastAPI and build systems you’re proud of — keep reading.
The Complete Series
Here is the Non Exhaustive list you need to know. If you are good with the basics, feel free to jump to specific part.
Part 1: The Modern Web & Why FastAPI Exists
Before you write a line of code, understand the landscape.
Why did FastAPI become the fastest-growing Python framework? What problems does it solve that Flask and Django don’t? This part covers the evolution from CGI scripts to async Python, and why type hints changed everything.
Key insight: FastAPI isn’t just fast — it’s fast to develop with. The automatic documentation alone saves hours per week.
Part 2: Type Hints & Pydantic: FastAPI’s Foundation
The secret sauce that makes everything else possible.
FastAPI’s magic comes from Pydantic. This part demystifies type hints, shows how Pydantic validates data at runtime, and explains why this matters for API development.
Key insight: Once you understand Pydantic, you understand 60% of FastAPI.
Part 3: Building Your First API: Routes, Requests, Responses
50 lines of code. A complete CRUD API.
No theory — just a working Task API with GET, POST, PATCH, DELETE. Path parameters, query parameters, request bodies, response models, status codes, error handling.
Key insight: FastAPI’s type system means you declare what you want, and the framework handles validation, serialization, and documentation automatically.
Part 4: The Three-Layer Architecture Pattern
The difference between a weekend project and production code.
Web → Service → Data. Why separating concerns matters, how to structure your project, and the exception handling pattern that keeps your layers clean.
Key insight: Your web layer should never know what database you’re using. Your data layer should never know about HTTP status codes.
Part 5: Async Deep Dive: When and Why
Async isn’t always faster. Here’s when it actually matters.
The event loop explained simply. CPU-bound vs I/O-bound operations. When async helps, when it hurts, and how FastAPI handles both sync and async functions.
Key insight: Use async def with async libraries, def with sync libraries. FastAPI’s thread pool handles the rest.
Part 6: Dependency Injection: The Clean Code Secret
Dependency injection sounds academic. In FastAPI, it’s practical magic.
Depends() is FastAPI’s superpower. Database sessions, authentication, configuration, ML model loading — all handled cleanly with dependency injection.
Key insight: Dependencies can depend on other dependencies. Chain them: get_db → get_current_user → get_admin_user → endpoint.
Part 7: Authentication & Authorization Done Right
Most auth tutorials teach you to be insecure. Let’s fix that.
Password hashing with bcrypt. OAuth2 with JWT tokens. Role-based access control. CORS configuration. And the six security mistakes I see in almost every codebase.
Key insight: Authentication (who are you?) and authorization (what can you do?) are different problems. Solve them separately.
Part 8: Testing FastAPI: From Unit to E2E
Untested code is legacy code.
The testing pyramid. pytest fixtures. Mocking dependencies. TestClient for API tests. Async testing gotchas. Coverage targets. GitHub Actions CI/CD.
Key insight: app.dependency_overrides is how you swap real databases for fakes in tests. No mocking gymnastics required.
Part 9: Database Patterns: SQLAlchemy & SQLModel
Your API is only as good as your data layer.
SQLModel’s Pydantic integration. Connection pooling. Async database access. Alembic migrations. The repository pattern.
Key insight: SQLModel lets you define one model for both your API and your database. No more duplicate classes.
Part 10: Production Deployment: Docker, K8s, Observability
Development is easy. Production is where frameworks prove themselves.
Multi-stage Docker builds (1.2GB → 150MB). Gunicorn + Uvicorn workers. Kubernetes deployments with health checks. Structured JSON logging. Prometheus metrics. OpenTelemetry tracing.
Key insight: If you can’t observe it, you can’t debug it. Metrics, logs, and traces aren’t optional in production.
What Makes This Different?
No magic without explanation. Every pattern includes why it works
Production-tested code. These patterns come from systems handling real traffic
Complete, not comprehensive. Covered what you need to build production APIs
Honest about tradeoffs. Async isn’t always better. ORMs aren’t always necessary.
Zero paid promotions — just patterns that work
Let’s Connect
Found a bug? Have a question? Want to share what you built?
If this series helped you, share it with someone who’s struggling with FastAPI. We’ve all been there.
Building production systems is hard. Learning from others’ mistakes makes it easier. This series is my attempt to share what I’ve learned — so you can skip the painful parts and build something great.
FastAPI From First Principles — Free Course was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.
