What this book covers

Chapter 1, Python Development Environment Setup, is aimed at setting up the development environment so that you can start working with Python and FastAPI. We’ll introduce the various tools that are commonly used in the Python community to ease development.

Chapter 2, Python Programming Specificities, introduces you to the specificities of programming in Python, specifically block indentation, control flow statements, exception handling, and the object-oriented paradigm. We’ll also cover features such as list comprehensions and generators. Finally, we’ll see how type hinting and asynchronous I/O work.

Chapter 3, Developing a RESTful API with FastAPI, covers the basics of the creation of a RESTful API with FastAPI: routing, parameters, request body validation, and response. We’ll also show how to properly structure a FastAPI project with dedicated modules and separate routers.

Chapter 4, Managing Pydantic Data Models in FastAPI, covers in more detail the definition of data models with Pydantic, the underlying data validation library used by FastAPI. We’ll explain how to implement variations of the same model without repeating ourselves, thanks to class inheritance. Finally, we’ll show how to implement custom data validation logic on those models.

Chapter 5, Dependency Injection in FastAPI, explains how dependency injection works and how we can define our own dependencies to reuse logic across different routers and endpoints.

Chapter 6, Databases and Asynchronous ORMs, demonstrates how we can set up a connection with a database to read and write data. We’ll cover how to use SQLAlchemy to work asynchronously with SQL databases and how they interact with the Pydantic model. Finally, we’ll also show you how to work with MongoDB, a NoSQL database.

Chapter 7, Managing Authentication and Security in FastAPI, shows us how to implement a basic authentication system to protect our API endpoints and return the relevant data for the authenticated user. We’ll also talk about the best practices around CORS and how to be safe from CSRF attacks.

Chapter 8, Defining WebSockets for Two-Way Interactive Communication in FastAPI, is aimed at understanding WebSockets and how to create them and handle the messages received with FastAPI.

Chapter 9, Testing an API Asynchronously with pytest and HTTPX, shows us how to write tests for our REST API endpoints.

Chapter 10, Deploying a FastAPI Project, covers the common configuration for running FastAPI applications smoothly in production. We’ll also explore several deployment options: PaaS platforms, Docker, and the traditional server setup.

Chapter 11, Introduction to Data Science in Python, gives a quick introduction to machine learning before moving on to two core libraries for data science in Python: NumPy and pandas. We’ll also show the basics of the scikit-learn library, a set of ready-to-use tools to perform machine learning tasks in Python.

Chapter 12, Creating an Efficient Prediction API Endpoint with FastAPI, shows how we can efficiently store a trained machine learning model using Joblib. Then, we’ll integrate it into a FastAPI backend, considering some technical details of FastAPI internals to achieve maximum performance. Finally, we’ll show a way to cache results using Joblib.

Chapter 13, Implementing a Real-Time Object Detection System Using WebSockets with FastAPI, implements a simple application to perform object detection in the browser, backed by a FastAPI WebSocket and a pre-trained computer vision model from the Hugging Face library.

Chapter 14, Creating a Distributed Text-to-Image AI System Using the Stable Diffusion Model, implements a system able to generate images from text prompts using the popular Stable Diffusion model. Since this task is a resource-intensive, slow process, we’ll see how to create a distributed system using worker queues that’ll stand behind our FastAPI backend and will perform the computations in the background.

Chapter 15, Monitoring the Health and Performance of a Data Science System, covers the extra mile so you are able to build robust, production-ready systems. One of the most important aspects to achieve this is to have all the data we need to ensure the system is operating correctly and detect as soon as possible when something goes wrong so we can take corrective actions. In this chapter, we’ll see how to set up a proper logging facility and how we can monitor the performance and health of our software in real time.