Complete REST API Development with FastAPI is a hands-on course in building a backend with FastAPI and uv, from endpoint design and PostgreSQL with SQLModel to JWT authentication, file uploads, a streaming AI endpoint, tests and Docker. It suits students, programming beginners and frontend developers moving into backend work, and you leave able to build your own REST API.
Today's apps and web sites talk to their back ends mainly through APIs, so building REST APIs has become a core skill for modern developers. FastAPI is a Python framework that is concise and fast, validates data automatically from type hints and generates API documentation instantly, which makes it suitable for beginners and a popular choice for APIs that connect to AI services.
This course takes learners who can write basic Python through building a complete backend with FastAPI. They start with the basics of HTTP and REST, set up the project with uv, receive and validate data with Pydantic and build CRUD endpoints, then connect PostgreSQL with SQLModel and Alembic. They go on to add login with OAuth2 and JWT, file uploads and an endpoint that calls an AI model and streams its answer, and finish by writing tests and running the whole system with Docker. (2 days, 6 hours per day, 12 hours in total, Beginner level.)
Course Details
This course is the 2026 edition of Complete REST API Development with Python FastAPI. It runs for 2 days, 6 hours per day (12 hours in total, 09:00-16:00), as lectures with labs. Beginner level. It keeps the original core of REST basics, CRUD, databases, JWT, file uploads, testing and Docker, but now connects to PostgreSQL 18 with SQLModel as the FastAPI documentation recommends, uses lifespan handlers instead of on_event, implements JWT with PyJWT
and pwdlib with Argon2, and adds a streaming AI endpoint with Server-Sent Events. The course does not use Django; learners who want the Django REST Framework should see Building REST APIs with Python, and those working with SQL Server should see FastAPI with MS SQL Server. The main tools are open source or free, and LLM APIs use the learner's own account. Learners take home a lab guide, source code and ready-to-use Docker files.
Section 1: Lab: API, HTTP and REST Basics
Section 2: Lab: Starting a FastAPI Project with uv
Section 3: Receiving and Validating Data with Pydantic
Section 4: Lab: CRUD and Project Structure
Section 5: Lab: Databases with SQLModel
Section 6: Migrations and Data Relationships
Section 7: Lab: Login with OAuth2 and JWT
Section 8: File Uploads and Middleware
Section 9: Lab: Adding AI to the API
Section 10: Workshop: Testing, Docker and Deployment
No public rounds are open right now. Join the waiting list and we will contact you first when the next round opens, or ask us on LINE. Or call 02-570-8449 or 088-807-9770
A REST API serves data over HTTP by treating each kind of data as a resource with its own URL. GET reads data, POST creates new data, PUT replaces a whole resource, PATCH updates part of it and DELETE removes it. Results are reported with status codes such as 200 for success, 201 for created, 404 for not found and 422 for data that fails validation, which FastAPI returns automatically when input does not match the model.
Create a project with uv init and install FastAPI with uv add "fastapi[standard]". In main.py, create app = FastAPI() and a function with a decorator such as @app.get("/hello") that returns a dict, then run uv run fastapi dev. Open /hello in the browser to see the result, and /docs to test it in the Swagger UI that FastAPI generates for you.
The user sends a username and password to a token endpoint. The system checks the password against the stored hash, using pwdlib with Argon2 in this course, and if it matches it issues a JWT with PyJWT containing the user and an expiry time. The user attaches the token as a Bearer value in the Authorization header on each request, and FastAPI verifies it through a dependency before allowing access to protected endpoints.
It suits students and programming beginners, frontend developers who want to do backend work, developers switching to backend roles and anyone who wants to build an API for their own app or AI service. You should be able to write basic Python, including variables, functions and importing libraries, and use a terminal and VS Code. No prior API or web development experience is needed.
You will be able to build your own REST API with FastAPI, from endpoint design and Pydantic validation to PostgreSQL with SQLModel and Alembic, JWT login, file uploads, a streaming AI endpoint, tests and running the system with Docker. Participants who complete the training receive a Certificate of Completion with its own number, which can be verified online.
Bring a laptop with uv (which can install Python 3.13 or later), VS Code and Docker Desktop for running PostgreSQL, plus Bruno or Postman for testing the API. These tools are free. The AI lab uses an LLM API from your own account, which may be charged by usage, or a small model running on your own machine instead.
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