Book Image

Getting Started with Streamlit for Data Science

By : Tyler Richards
Book Image

Getting Started with Streamlit for Data Science

By: Tyler Richards

Overview of this book

Streamlit shortens the development time for the creation of data-focused web applications, allowing data scientists to create web app prototypes using Python in hours instead of days. Getting Started with Streamlit for Data Science takes a hands-on approach to helping you learn the tips and tricks that will have you up and running with Streamlit in no time. You'll start with the fundamentals of Streamlit by creating a basic app and gradually build on the foundation by producing high-quality graphics with data visualization and testing machine learning models. As you advance through the chapters, you’ll walk through practical examples of both personal data projects and work-related data-focused web applications, and get to grips with more challenging topics such as using Streamlit Components, beautifying your apps, and quick deployment of your new apps. By the end of this book, you’ll be able to create dynamic web apps in Streamlit quickly and effortlessly using the power of Python.
Table of Contents (17 chapters)
1
Section 1: Creating Basic Streamlit Applications
7
Section 2: Advanced Streamlit Applications
11
Section 3: Streamlit Use Cases

Chapter 8: Deploying Streamlit Apps with Heroku and AWS

In Chapter 5, Deploying Streamlit with Streamlit Sharing, we learned how to deploy our Streamlit applications with Streamlit Sharing. Streamlit Sharing is quick, easy, and very effective for most applications but has a few downsides, mainly that we are limited by only being able to host three free applications at once and that we also are limited in the computational power at hand. The following excerpt is from the Streamlit Sharing page:

Apps get up to 1 CPU, 800 MB of RAM, and 800 MB of dedicated storage in a shared execution environment.

If you are in a situation where you want to deploy more than three applications at a time, or you want more compute as you run, for example, more complex ML models that would benefit from a GPU or more RAM, then this chapter is for you! We will cover how to set up accounts with AWS and Heroku and how to fully deploy your Streamlit applications there. 

In this chapter, we will...