Developing scalable APIs with Python - with FastAPI and Flask
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Machine Learning Engineer
This hands-on course teaches how to develop powerful and scalable APIs with Python. The two popular frameworks FastAPI and Flask are compared in order to understand their respective strengths and areas of application. Using hands-on exercises, participants will build their own RESTful APIs, optimize them for performance and scalability and gain a solid understanding of modern API architectures in Python.
1. overview and comparison: Flask vs. FastAPI
- History and objectives of the frameworks
- Synchronous vs. asynchronous programming model
- Differences in routing, middleware & dependency injection
- Community, extensibility & ecosystem
- When is which framework suitable?
2. architecture of modern, scalable APIs
- Layered Architecture: Routers, Services, Repositories
- API design principles (REST, stateless, idempotent)
- OpenAPI / Swagger support out-of-the-box
- Separation of business/infrastructure logic
- Caching, rate limiting & authentication
3. asynchronous processing & performance tuning
- Introduction to
- async/await
- in Python
- WSGI vs. ASGI - Differences & Implications
- Advantages of async I/O (e.g. for database access)
- Use of background tasks & queues
- Server comparison: Uvicorn, Gunicorn & Hypercorn
4. practical development of RESTful APIs
- Project structuring for Flask & FastAPI
- Define route, path and query parameters
- CRUD endpoints using the example of a ToDo app or a store
- Database connection with SQLAlchemy or Tortoise ORM
- Integration of external APIs / services
5. validation, serialization & documentation
- Introduction to Pydantic: Typing & Validation
- Creation of request/response models
- Automatic OpenAPI documentation (Swagger UI)
- Use of tags/description texts/examples
- Efficient data validation
6. focus on testability & expandability
- Unit/integration tests for API endpoints
- Use test clients for FastAPI/Flask
- Mocking external dependencies
- Structuring for better expandability (blueprints/routers)
- Dependency Injection
7. benchmarking & scaling strategies
- Use tools such as ab, wrk, locust or k6
- Runtime performance comparison between Flask/FastAPI
- Scaling with Gunicorn/Uvicorn Workers/Docker/Kubernetes
- Load balancing/horizontal scaling
- Health checks/readiness probes
8. best practices for production-ready projects
- Logging/Metrics/Tracing e.g., Prometheus/OpenTelemetry
- Exception handling (global error handler structured output)
- Security measures such as HTTPS/CORS/Auth
- CI/CD Pipelines
Requirement: Basic knowledge of Python programming
- Gain an overview of Flask vs. FastAPI and compare them
- Understanding the architecture of modern, scalable APIs
- Learning asynchronous processing and performance tuning
- Practical development of REST APIs with both frameworks
- Validate, serialize and document with Pydantic and Swagger
- Focus on testability and expandability
- Understanding benchmarking and scaling scenarios
- Get to know best practices for production-ready API projects
This course creates a sound understanding of the two web frameworks through an optimal mix of short theory blocks, guided exercises, practical examples and hands-on scenarios.
This course is ideal for developers who want to develop clean, maintainable and performant interfaces with Python using the two powerful web frameworks FastAPI and Flask.
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