9781803232959_

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 models on Hugging Face, and even creating apps for job interviews!

Key Features

Learn how to showcase machine learning models with Streamlit quickly and effectively
Work your way through hands-on exercises to become an expert Streamlit creator
Discover the full range of Streamlits capabilities to effortlessly create and deploy beautiful apps

Book Description

If you work with data in Python and are looking to create data apps that showcase ML models and make interactive visualizations that are easy to create, then this is the ideal book for you. Streamlit for Data Science, Second Edition, shows you how to create and deploy widgets and dashboards quickly, all within Python. This helps you create prototypes in hours instead of days!

Written by a prolific Streamlit user, and current employee who joined after the first edition was published, 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, deploying Streamlit on Hugging Face, and a totally updated code repository on GitHub to help you practice your newfound skills.

Youll start your journey with the fundamentals of Streamlit and gradually build on the 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, youll 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, AWS, and Heroku
Integrate Streamlit with Hugging Face, 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 youll get the most out of this book if youve used Python libraries like Pandas and NumPy in the past.

An Introduction to Streamlit
Uploading, Downloading, and Manipulating Data
Data Visualization
Using Machine Learning with Streamlit
Deploying Streamlit with Streamlit Sharing
Beautifying Streamlit Apps
Exploring Streamlit Components
Deploying Streamlit Apps with Heroku and AWS
Connecting Streamlit to Databases
Improving Job Applications with Streamlit
The Data Project Prototyping Projects in Streamlit
Using Streamlit for Teams
Streamlit Power Users

...(*_*)9781803232959_

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 Streamlits capabilities via hands-on exercises to effortlessly create and deploy well-designed apps

Book Description

If 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. Youll 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, youll 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

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Key Features

Book Description

What you will learn

Who This Book Is For

Table of Contents

Key Features

Book Description

What you will learn

Who this book is for

Streamlit for Data Science

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