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Building Data Science Applications with FastAPI

Building Data Science Applications with FastAPI

By : François Voron
4.7 (16)
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Building Data Science Applications with FastAPI

Building Data Science Applications with FastAPI

4.7 (16)
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)
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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

Chapter 1: Python Development Environment Setup

Before we can get started on our FastAPI journey, we need to configure a clean and efficient Python environment. This chapter will show you the best practices and conventions that Python developers use daily to run their projects.

By the end of this chapter, you'll be able to run Python projects and install third-party dependencies in a contained environment that won't raise conflicts if you happen to work on another project that uses different versions of the Python language or different dependencies.

In this chapter, we're going to cover the following main topics:

  • Installing a Python distribution using pyenv
  • Creating a Python virtual environment
  • Installing Python packages with pip
  • Installing the HTTPie command-line utility
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Building Data Science Applications with FastAPI
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