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

Chapter 12: Training Machine Learning Models with scikit-learn

As we mentioned in the introduction of the previous chapter, Python has gained a lot of popularity in the data science field. We've seen that libraries such as NumPy and pandas have emerged to handle big datasets efficiently in Python. Those libraries are the foundation for libraries dedicated to machine learning (ML), such as the famous scikit-learn library, a complete toolset for implementing most of the algorithms and techniques that are used daily by data scientists. In this chapter, we'll provide a quick introduction to ML, what it is about, what it tries to solve, and how. Then, we'll learn how to use scikit-learn to train and test ML models. We'll also have a deeper look at two classical ML models, Naive Bayes models and support vector machines, both of which can perform surprisingly well if used correctly.

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

  • What is...