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Building Data Science Applications with FastAPI

Building Data Science Applications with FastAPI - Second Edition

By : Voron
4.2 (9)
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Building Data Science Applications with FastAPI

Building Data Science Applications with FastAPI

4.2 (9)
By: Voron

Overview of this book

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.
Table of Contents (21 chapters)
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1
Part 1: Introduction to Python and FastAPI
7
Part 2: Building and Deploying a Complete Web Backend with FastAPI
13
Part 3: Building Resilient and Distributed Data Science Systems with FastAPI

Introduction to Data Science in Python

In recent years, Python has gained a lot of popularity in the data science field. Its very efficient and readable syntax makes the language a very good choice for scientific research, while still being suitable for production workloads; it’s very easy to deploy research projects into real applications that will bring value to users. Thanks to this growing interest, a lot of specialized Python libraries have emerged and are now standards in the industry. In this chapter, we’ll introduce the fundamental concepts of machine learning before diving into the Python libraries used daily by data scientists.

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

  • Understanding the basic concepts of machine learning
  • Creating and manipulating NumPy arrays and pandas datasets
  • Training and evaluating machine learning models with scikit-learn
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83
Tech Concepts
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Programming languages
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Building Data Science Applications with FastAPI
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