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Building Data Science Applications with FastAPI
Building Data Science Applications with FastAPI

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

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Can$44.99 Can$50.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.2 (9 Ratings)
eBook Jul 2023 422 pages 2nd Edition
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Can$44.99 Can$50.99
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Profile Icon Voron
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Can$44.99 Can$50.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.2 (9 Ratings)
eBook Jul 2023 422 pages 2nd Edition
eBook
Can$44.99 Can$50.99
Paperback
Can$63.99
Subscription
Free Trial
eBook
Can$44.99 Can$50.99
Paperback
Can$63.99
Subscription
Free Trial

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Building Data Science Applications with FastAPI

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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.

Who is this book for?

This book is for data scientists and software developers interested in gaining knowledge of FastAPI and its ecosystem to build data science applications. Basic knowledge of data science and machine learning concepts and how to apply them in Python is recommended.

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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jul 31, 2023
Length: 422 pages
Edition : 2nd
Language : English
ISBN-13 : 9781837637263
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Product Details

Publication date : Jul 31, 2023
Length: 422 pages
Edition : 2nd
Language : English
ISBN-13 : 9781837637263
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Table of Contents

20 Chapters
Part 1: Introduction to Python and FastAPI Chevron down icon Chevron up icon
Chapter 1: Python Development Environment Setup Chevron down icon Chevron up icon
Chapter 2: Python Programming Specificities Chevron down icon Chevron up icon
Chapter 3: Developing a RESTful API with FastAPI Chevron down icon Chevron up icon
Chapter 4: Managing Pydantic Data Models in FastAPI Chevron down icon Chevron up icon
Chapter 5: Dependency Injection in FastAPI Chevron down icon Chevron up icon
Part 2: Building and Deploying a Complete Web Backend with FastAPI Chevron down icon Chevron up icon
Chapter 6: Databases and Asynchronous ORMs Chevron down icon Chevron up icon
Chapter 7: Managing Authentication and Security in FastAPI Chevron down icon Chevron up icon
Chapter 8: Defining WebSockets for Two-Way Interactive Communication in FastAPI Chevron down icon Chevron up icon
Chapter 9: Testing an API Asynchronously with pytest and HTTPX Chevron down icon Chevron up icon
Chapter 10: Deploying a FastAPI Project Chevron down icon Chevron up icon
Part 3: Building Resilient and Distributed Data Science Systems with FastAPI Chevron down icon Chevron up icon
Chapter 11: Introduction to Data Science in Python Chevron down icon Chevron up icon
Chapter 12: Creating an Efficient Prediction API Endpoint with FastAPI Chevron down icon Chevron up icon
Chapter 13: Implementing a Real-Time Object Detection System Using WebSockets with FastAPI Chevron down icon Chevron up icon
Chapter 14: Creating a Distributed Text-to-Image AI System Using the Stable Diffusion Model Chevron down icon Chevron up icon
Chapter 15: Monitoring the Health and Performance of a Data Science System Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.2
(9 Ratings)
5 star 55.6%
4 star 11.1%
3 star 33.3%
2 star 0%
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Vinod Sangare Aug 25, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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 Amazon
Vladimir Nabokov Jan 30, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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 Amazon
S.Kundu Sep 05, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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 Amazon
Rahul Bahadur Sep 09, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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 Amazon
Om S Aug 13, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
"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 Amazon
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