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Building Data Science Applications with FastAPI

Building Data Science Applications with FastAPI - Second Edition

By : François Voron
4.3 (10)
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Building Data Science Applications with FastAPI

Building Data Science Applications with FastAPI

4.3 (10)
By: François 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

Training models with scikit-learn

scikit-learn is one of the most widely used Python libraries for data science. It implements dozens of classic ML models, but also numerous tools to help you while training them, such as preprocessing methods and cross-validation. Nowadays, you’ll probably hear about more modern approaches, such as PyTorch, but scikit-learn is still a solid tool for a lot of use cases.

The first thing you must do to get started is to install it in your Python environment:

(venv) $ pip install scikit-learn

We can now start our scikit-learn journey!

Training models and predicting

In scikit-learn, ML models and algorithms are called estimators. Each is a Python class that implements the same methods. In particular, we have fit, which is used to train a model, and predict, which is used to run the trained model on new data.

To try this, we’ll load a sample dataset. scikit-learn comes with a few toy datasets that are very useful for performing...

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