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

The standard ML workflow

The first step to creating an app that uses ML is the ML model itself. There are dozens of popular workflows for creating your own ML models. It's likely you might have your own already! There are two parts of this process to consider:

  • The generation of the ML model
  • The use of the ML model in production 

If the plan is to train a model once and then use this model in our Streamlit app, the best method is to create this model outside of Streamlit (for example, in a Jupyter notebook or in a standard Python file) first, and then use this model within the app. 

If the plan is to use the user input to train the model inside our app, then we can no longer create the model outside of Streamlit and instead will need to run the model training within the Streamlit app. 

We will start by building our ML models outside of Streamlit and move on to training our models inside of Streamlit apps after.