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FastAPI Cookbook

FastAPI Cookbook

By : Giunio De Luca
4.3 (4)
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FastAPI Cookbook

FastAPI Cookbook

4.3 (4)
By: Giunio De Luca

Overview of this book

FastAPI is a cutting-edge Python framework that is revolutionizing the way web apps and APIs are built. Known for its speed, simplicity, and scalability, FastAPI empowers developers to create high-performing applications with ease. This book will help you leverage FastAPI’s immense potential to handle high-traffic scenarios and integrate seamlessly with modern Python tools. The book begins by familiarizing you with the basics of setting up and configuring your FastAPI environment before moving to the intricacies of building RESTful APIs, managing data with SQL and NoSQL databases, and handling authentication and authorization. Next, you'll focus on advanced topics such as custom middleware, WebSocket communication, and integration with various Python libraries. Each chapter is meticulously crafted with practical recipes, progressing from foundational concepts to advanced features and best practices. The concluding chapters show you how to optimize performance, implement rate limiting, and execute background tasks, empowering you to become a proficient FastAPI developer. By the end of this book, you'll have gained the skills you need to migrate existing apps to FastAPI, and be equipped to tackle any challenge in the modern web development landscape, ensuring your apps are not only functional, but also efficient, secure, and scalable.
Table of Contents (15 chapters)
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Using ML models with Joblib

ML models are powerful tools for data analysis, prediction, and decision-making in various applications. FastAPI provides a robust framework for building web services, making it an ideal choice for deploying ML models in production environments. In this recipe, we will see how to integrate an ML model with FastAPI using Joblib, a popular library for model serialization and deserialization in Python.

We will develop an AI-powered doctor application that can diagnose diseases by analyzing the symptoms provided.

Warning

Note that the diagnoses provided by the AI doctor should not be trusted in real-life situations, as it is not reliable.

Getting ready

Prior knowledge of ML is not mandatory but having some can be useful to help you follow the recipe.

We will apply the recipe to a new project, so create a folder named ai_doctor that we will use as the project root folder.

To ensure that you have all the necessary packages in your environment...

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