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FastAPI Cookbook

You're reading from   FastAPI Cookbook Develop high-performance APIs and web applications with Python

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Product type Paperback
Published in Aug 2024
Publisher Packt
ISBN-13 9781805127857
Length 358 pages
Edition 1st Edition
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Author (1):
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Giunio De Luca Giunio De Luca
Author Profile Icon Giunio De Luca
Giunio De Luca
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Toc

Table of Contents (15) Chapters Close

Preface 1. Chapter 1: First Steps with FastAPI 2. Chapter 2: Working with Data FREE CHAPTER 3. Chapter 3: Building RESTful APIs with FastAPI 4. Chapter 4: Authentication and Authorization 5. Chapter 5: Testing and Debugging FastAPI Applications 6. Chapter 6: Integrating FastAPI with SQL Databases 7. Chapter 7: Integrating FastAPI with NoSQL Databases 8. Chapter 8: Advanced Features and Best Practices 9. Chapter 9: Working with WebSocket 10. Chapter 10: Integrating FastAPI with other Python Libraries 11. Chapter 11: Middleware and Webhooks 12. Chapter 12: Deploying and Managing FastAPI Applications 13. Index 14. Other Books You May Enjoy

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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