You're reading from Building Data Science Applications with FastAPI Develop, manage, and deploy efficient machine learning applications with Python

Product type Paperback

Published in Oct 2021

Publisher Packt

ISBN-13 9781801079211

Length 426 pages

Edition 1st Edition

Languages

Python

Tools

FastAPI

Concepts

Data Science

Author (1):

François Voron

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Table of Contents (19) Chapters

Preface

1. Section 1: Introduction to Python and FastAPI

2. Chapter 1: Python Development Environment Setup FREE CHAPTER

3. Chapter 2: Python Programming Specificities

4. Chapter 3: Developing a RESTful API with FastAPI

5. Chapter 4: Managing Pydantic Data Models in FastAPI

6. Chapter 5: Dependency Injections in FastAPI

7. Section 2: Build and Deploy a Complete Web Backend with FastAPI

8. Chapter 6: Databases and Asynchronous ORMs

9. Chapter 7: Managing Authentication and Security in FastAPI

10. Chapter 8: Defining WebSockets for Two-Way Interactive Communication in FastAPI

11. Chapter 9: Testing an API Asynchronously with pytest and HTTPX

12. Chapter 10: Deploying a FastAPI Project

13. Section 3: Build a Data Science API with Python and FastAPI

14. Chapter 11: Introduction to NumPy and pandas

15. Chapter 12: Training Machine Learning Models with scikit-learn

16. Chapter 13: Creating an Efficient Prediction API Endpoint with FastAPI

17. Chapter 14: Implement a Real-Time Face Detection System Using WebSockets with FastAPI and OpenCV

18. Other Books You May Enjoy

Configuring CORS and protecting against CSRF attacks

Nowadays, lot of software are designed to be used in a browser through an interface built with HTML, CSS, and JavaScript. Traditionally, web servers were responsible for handling browser requests and returning an HTML response, ready to be shown. This is a common use case for frameworks such as Django.

For a few years now, there has been a shift in that pattern. With the emergence of JavaScript frameworks, such as Angular, React, and Vue, we tend to have a clear separation between the frontend, a highly interactive user interface powered by JavaScript, and the backend. Thus, those backends are now only responsible for data storage and retrieving and executing business logic. This is a task that REST APIs are very good at! From the JavaScript code, the user interface can then just spawn requests to your API and handle the result to present it.

However, we must still handle authentication: we want our user to be able to log in...

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You're reading from Building Data Science Applications with FastAPI Develop, manage, and deploy efficient machine learning applications with Python

Table of Contents (19) Chapters

Configuring CORS and protecting against CSRF attacks

Authors (1)

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You're reading from Building Data Science Applications with FastAPI Develop, manage, and deploy efficient machine learning applications with Python

Table of Contents (19) Chapters

Configuring CORS and protecting against CSRF attacks

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