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

Chapter 4: Using Machine Learning with Streamlit

A very common situation data scientists find themselves in is at the end of the model creation process, not knowing exactly how to convince non-data scientists that their model is worthwhile. They might have performance metrics from their model or some static visualizations but have no easy way to allow others to interact with their model. 

Before Streamlit, there were a couple of other options, the most popular being creating a full-fledged app in Flask or Django or turning their model into an Application Programming Interface (API) and pointing developers toward it. These are great options but tend to be time-consuming and suboptimal for valuable use cases such as prototyping an app.

The incentives on teams are a little misaligned here. A data scientist wants to create the best models for their teams, but if they need to take a day or two (or, if they have experience, a few hours) of work to turn their model into a Flask...