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Key benefits

Uncover the secrets of FastAPI, including async I/O, type hinting, and dependency injection

Learn to add authentication, authorization, and interaction with databases in a FastAPI backend

Develop real-world projects using pre-trained AI models

Description

Building Data Science Applications with FastAPI is the go-to resource for creating efficient and dependable data science API backends. This second edition incorporates the latest Python and FastAPI advancements, along with two new AI projects – a real-time object detection system and a text-to-image generation platform using Stable Diffusion. The book starts with the basics of FastAPI and modern Python programming. You'll grasp FastAPI's robust dependency injection system, which facilitates seamless database communication, authentication implementation, and ML model integration. As you progress, you'll learn testing and deployment best practices, guaranteeing high-quality, resilient applications. Throughout the book, you'll build data science applications using FastAPI with the help of projects covering common AI use cases, such as object detection and text-to-image generation. These hands-on experiences will deepen your understanding of using FastAPI in real-world scenarios. By the end of this book, you'll be well equipped to maintain, design, and monitor applications to meet the highest programming standards using FastAPI, empowering you to create fast and reliable data science API backends with ease while keeping up with the latest advancements.

What you will learn

Explore the basics of modern Python and async I/O programming

Get to grips with basic and advanced concepts of the FastAPI framework

Deploy a performant and reliable web backend for a data science application

Integrate common Python data science libraries into a web backend

Integrate an object detection algorithm into a FastAPI backend

Build a distributed text-to-image AI system with Stable Diffusion

Add metrics and logging and learn how to monitor them

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

Machine Learning Engineering with Python

Can$63.99

Can$69.99

Building Data Science Applications with FastAPI

Can$63.99

Total Can$ 197.97

Vinod Sangare Aug 25, 2023

As data science proliferates, the need for reliable and well-designed APIs to serve machine learning models grows. This new book provides Python developers with an accessible guide to creating performant, production-ready APIs for data science using the FastAPI framework.Written by experienced practitioners, the book aims to teach readers end-to-end implementation of performant data science backends. The authors recognize that while many resources explain Python machine learning concepts, practical guidance on building maintainable APIs is scarce. This book fills that gap with concrete examples using modern Python tools like FastAPI, Pydantic, and SQLAlchemy.The book covers FastAPI basics like routing, validation, and dependency injection before diving into advanced topics like authentication, databases, and real-time features. Two full projects showcase applying the concepts: an object detection system using WebSockets and a text-to-image generator with a distributed backend. The hands-on approach teaches not just FastAPI usage but also API design best practices.Importantly, the book goes beyond basics to tackle critical real-world aspects of robust APIs like testing, monitoring, and deployment. The authors understand that clean code is only part of the equation - things like logging, performance metrics, and containerization are essential for production systems. This focus on practical engineering makes the book invaluable for readers building their first serious data science service.While focusing on FastAPI, the book aptly explains foundational Python programming topics like asynchronous programming, object-oriented design, and type hinting for readers with basic data science Python skills. The accessible writing style introduces complex concepts clearly without oversimplifying.Ultimately, this book succeeds as a comprehensive modern guide to crafting production-ready data science APIs. It balances theoretical understanding with practical engineering principles. Readers will learn how FastAPI simplifies API development while gaining skills for creating maintainable, high-quality services. For Python developers looking to level up their API building abilities, this book is an indispensable resource.

Amazon Verified review

Vladimir Nabokov Jan 30, 2024

Learned a lot from this. Very nicely explained.I've been doing data science for years. This book helps me to package data science solutions better for handover to UI or mlops.

Amazon Verified review

S.Kundu Sep 05, 2023

The initial section of the book will start with basic Python programming along with introduction to FastAPI including with working with Pydantic objects. It will also teach you about Dependency Injection in FastAPI.The book will then slowly introduce the concepts of different databases where you will learn how to create models including authentication mechanism and security in FastAPI. You will learn how to test an API and how to deploy a FastAPI project using Docker.On the final section the book will help you in building resilient and distributed data science systems with FastAPI. You will create prediction API endpoint using FastAPI and also implement a real time object detection system using WebSockets with FastAPI. You will also learn how to monitor the health and performance of a Data Science system.

Amazon Verified review

Rahul Bahadur Sep 09, 2023

I have seen a lot of data science applications that don't follow the best practices - in terms of not having a data model, logging metrics efficiently, etc. I recommend everyone who is developing APIs refer this. This book is for everyone - whether you are starting off with API development in Python or, are a seasoned professional trying to incorporate best practices. This book guides you through the basics of API development using FastAPI and Pydantic + Python Data Science stack (numpy + scikit learn) along with modern generative AI models (like Stable Diffusion) and later delves into more advanced concepts like distributed serving (using Dramatiq) and logging using Loguru + Grafana for displaying the logs.The author has given plenty of code examples so that it is super easy to follow along and also mentions other books you might want to check out if you want to specialize in this.This book, personally, has been a great help in my work in reducing some of the technical debt around efficient APIs.

Amazon Verified review

Om S Aug 13, 2023

"Building Data Science Applications with FastAPI" is an indispensable guide that unlocks the full potential of FastAPI for creating robust data science and AI applications. This second edition, enriched with the latest Python and FastAPI advancements, propels readers into a world of efficient backend development.Navigating from Python essentials to advanced FastAPI features, this book delivers a comprehensive understanding of the framework. It seamlessly integrates key components like authentication, databases, and AI models, while also covering testing and deployment methodologies.What truly sets this book apart are its practical AI projects – real-time object detection and text-to-image generation with Stable Diffusion. By immersing readers in hands-on experiences, it bridges the gap between theory and application.Perfect for data scientists and developers, the book empowers readers to create high-performance data science API backends that meet the highest programming standards. From modern Python practices to distributed AI systems, this book is your ultimate toolkit for mastering FastAPI's potential.

Amazon Verified review

Building Data Science Applications with FastAPI: Develop, manage, and deploy efficient machine learning applications with Python , Second Edition

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