An easy-to-follow and comprehensive guide to creating data apps with Streamlit, including how-to guides for working with cloud data warehouses like Snowflake, using pretrained Hugging Face and OpenAI models, and creating apps for job interviews.

Key Features

• Create machine learning apps with random forest, Hugging Face, and GPT-3.5 turbo models
• Gain an insight into how experts harness Streamlit with in-depth interviews with Streamlit power users
• Discover the full range of Streamlit’s capabilities via hands-on exercises to effortlessly create and deploy well-designed apps

Book DescriptionIf you work with data in Python and are looking to create data apps that showcase ML models and make beautiful interactive visualizations, then this is the ideal book for you. Streamlit for Data Science, Second Edition, shows you how to create and deploy data apps quickly, all within Python. This helps you create prototypes in hours instead of days! Written by a prolific Streamlit user and senior data scientist at Snowflake, this fully updated second edition builds on the practical nature of the previous edition with exciting updates, including connecting Streamlit to data warehouses like Snowflake, integrating Hugging Face and OpenAI models into your apps, and connecting and building apps on top of Streamlit databases. Plus, there is a totally updated code repository on GitHub to help you practice your newfound skills. You'll start your journey with the fundamentals of Streamlit and gradually build on this foundation by working with machine learning models and producing high-quality interactive apps. The practical examples of both personal data projects and work-related data-focused web applications will help you get to grips with more challenging topics such as Streamlit Components, beautifying your apps, and quick deployment. By the end of this book, you'll be able to create dynamic web apps in Streamlit quickly and effortlessly. What you will learn

• Set up your first development environment and create a basic Streamlit app from scratch
• Create dynamic visualizations using built-in and imported Python libraries
• Discover strategies for creating and deploying machine learning models in Streamlit
• Deploy Streamlit apps with Streamlit Community Cloud, Hugging Face Spaces, and Heroku
• Integrate Streamlit with Hugging Face, OpenAI, and Snowflake
• Beautify Streamlit apps using themes and components
• Implement best practices for prototyping your data science work with Streamlit

Who this book is for

This book is for data scientists and machine learning enthusiasts who want to get started with creating data apps in Streamlit. It is terrific for junior data scientists looking to gain some valuable new skills in a specific and actionable fashion and is also a great resource for senior data scientists looking for a comprehensive overview of the library and how people use it. Prior knowledge of Python programming is a must, and you’ll get the most out of this book if you’ve used Python libraries like Pandas and NumPy in the past.