Thoughts, tutorials, and insights on engineering and programming.
Learn how to serve a trained machine learning model with FastAPI, prepare features from user input, generate real-time predictions, and connect the API to a Streamlit frontend.
Learn how to update and delete existing resources with FastAPI and complete the CRUD operations of a Patient Management System.
Learn how FastAPI handles request bodies, accepts structured data through POST requests, validates it with Pydantic, and creates new resources.
Learn how Pydantic creates type-safe data models, validates input automatically, and handles advanced validation, nested models, computed fields, and serialization.
Learn how to use path parameters, query parameters, HTTP status codes, and error handling to build more flexible FastAPI endpoints.
Understand how HTTP methods work, how clients communicate with servers, and how GET, POST, PUT, and DELETE map to CRUD operations in FastAPI.
Learn how FastAPI works under the hood, set up your first application, run it with Uvicorn, and explore its automatic API documentation.
APIs are the bridge between applications, backend systems, databases, and machine learning models. Let’s understand how they work before building one with FastAPI.
