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)
Section 1: Creating Basic Streamlit Applications
Section 2: Advanced Streamlit Applications
Section 3: Streamlit Use Cases

Deploying Streamlit with AWS

In comparison to deploying with Heroku, deploying apps on AWS is significantly more cumbersome but has seemingly infinite options. There are a few steps to deploying your own apps with AWS, and these include the following:

  1. Selecting and launching a virtual machine
  2. Installing the necessary software
  3. Cloning and running your app
  4. Long-term AWS deployment

We will run through these sequentially!

Selecting and launching a virtual machine

AWS has literally hundreds of service options for everything from deploying ML models to compute resources to everything in between. In this book so far, we have referred to the services listed in the following screenshot under the central name AWS, but to be more precise, we are going to be using Amazon Elastic Compute Cloud, or Amazon EC2 for short. This next screenshot shows the breadth of services available just for compute resources, which does not include any of the services available...