Book Image

Building Data Science Applications with FastAPI

By : François Voron
5 (1)
Book Image

Building Data Science Applications with FastAPI

5 (1)
By: François Voron

Overview of this book

FastAPI is a web framework for building APIs with Python 3.6 and its later versions based on standard Python-type hints. With this book, you’ll be able to create fast and reliable data science API backends using practical examples. This book starts with the basics of the FastAPI framework and associated modern Python programming language concepts. You'll be taken through all the aspects of the framework, including its powerful dependency injection system and how you can use it to communicate with databases, implement authentication and integrate machine learning models. Later, you’ll cover best practices relating to testing and deployment to run a high-quality and robust application. You’ll also be introduced to the extensive ecosystem of Python data science packages. As you progress, you’ll learn how to build data science applications in Python using FastAPI. The book also demonstrates how to develop fast and efficient machine learning prediction backends and test them to achieve the best performance. Finally, you’ll see how to implement a real-time face detection system using WebSockets and a web browser as a client. By the end of this FastAPI book, you’ll have not only learned how to implement Python in data science projects but also how to maintain and design them to meet high programming standards with the help of FastAPI.
Table of Contents (19 chapters)
1
Section 1: Introduction to Python and FastAPI
7
Section 2: Build and Deploy a Complete Web Backend with FastAPI
13
Section 3: Build a Data Science API with Python and FastAPI

Type hinting and type checking with mypy

In the first section of this chapter, we said that Python was a dynamically typed language: the interpreter doesn't check types at compile time but rather at runtime. This makes the language a bit more flexible and the developer a bit more efficient. However, if you are experienced with that kind of language, you probably know that it's easy to produce errors and bugs in this context: forgetting arguments and type mismatch.

This is why Python introduced type hinting starting with version 3.5. The goal is to provide a syntax to annotate the source code with type annotations: each variable, function, and class can be annotated to give indications about the types they expect. This doesn't mean that Python becomes a statically typed language. Those annotations remain completely optional and are ignored by the interpreter. However, those annotations can be used by static-type checkers, which will check whether your code is valid...