Bridging Theory and Practice: ML Solutions for Today’s Challenges

3 days, 20+ experts, and 25+ tech sessions and talks covering critical aspects of:

• Agentic and Generative AI
• Applied Machine Learning in the Real World
• ML Engineering and Optimization

👉 Book your ticket now >>

The future of AI is unfolding. Don’t fall behind.

Stay ahead with DataPro, the free weekly newsletter for data scientists, AI/ML researchers, and data engineers.
From trending tools like PyTorch, scikit-learn, XGBoost, and BentoML to hands-on insights on database optimization and real-world ML workflows, you’ll get what matters, fast.

Stay sharp with DataPro. Join 115K+ data professionals who never miss a beat.

Business runs on data. Make sure yours tells the right story.

BIPro is your free weekly newsletter for BI professionals, analysts, and data leaders.
Get practical tips on dashboarding, data visualization, and analytics strategy with tools like Power BI, Tableau, Looker, SQL, and dbt.

Get smarter with BIPro. Trusted by 35K+ BI professionals, see what you’re missing.

Code: USD75OFF

This is the code repository for Building Data Science Applications with FastAPI -Second Edition, published by Packt.

Develop, manage, and deploy efficient machine learning applications with Python

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.

This book covers the following exciting features:

• 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

If you feel this book is for you, get your copy today!

All of the code is organized into folders.

The code will look like the following:

from fastapi import FastAPI

app = FastAPI()

@app.get("/users/{type}/{id}")
async def get_user(type: str, id: int):
    return {"type": type, "id": id}

Following is what you need for this book: 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.

With the following software and hardware list you can run all code files present in the book.

We’ll mainly work with the Python programming language. The first chapter will explain how to set up a proper Python environment on your operating system. Some examples also involve running web pages with JavaScript, so you’ll need a modern browser such as Google Chrome or Mozilla Firefox. In Chapter 14, we’ll run the Stable Diffusion model, which requires a powerful machine. We recommend a computer with 16 GB of RAM and a modern NVIDIA GPU to be able to generate good-looking images. System requirements are mentioned in the following table:

• Applied Geospatial Data Science with Python [Packt] [Amazon]

• Building Data Science Solutions with Anaconda [Packt] [Amazon]

François Voron graduated from the University of Saint-Étienne (France) and the University of Alicante (Spain) with a master’s degree in machine learning and data mining. A full stack web developer and a data scientist, François has a proven track record working in the SaaS industry, with a special focus on Python backends and REST APIs. He is also the creator and maintainer of FastAPI Users, the #1 authentication library for FastAPI, and is one of the top experts in the FastAPI community