Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Building Data Science Applications with FastAPI
  • Toc
  • feedback
Building Data Science Applications with FastAPI

Building Data Science Applications with FastAPI

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

Building Data Science Applications with FastAPI

4.7 (16)
By: 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)
close
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

Summary

Great! You now have a grasp of the ins and outs of NumPy and pandas. Basically, those libraries are the essential tool for data scientists in Python. By relying on optimized and compiled code, they allow you to load and manipulate large set of data in Python, without sacrificing performance. To allow this, they define fixed-type data structures, meaning each value in the dataset should be of the same type. This is what enables efficient memory consumption and fast computations.

Even though those basics should be enough for you to get started, we recommend that you spend some time on the official user guides and tinker with those a bit to discover all their aspects.

As we said in the introduction, NumPy and pandas are at the heart of most data science applications in Python. In the next chapter, we'll see how they will help us in machine learning tasks, along with the well-known machine learning library scikit-learn.

bookmark search playlist download font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete