Chapter 5: Deploying Streamlit with Streamlit Sharing

So far in this book, we have focused on Streamlit app development, from creating complex visualizations to deploying and creating machine learning (ML) models. In this chapter, we will learn how to deploy these applications so they can be shared with anyone with internet access. This is a crucial part of Streamlit apps, as, without the ability to deploy a Streamlit app, the friction still exists for users or consumers of your work. If we believe that Streamlit removes the friction between creating data science analysis/products/models and sharing them with others, then we must also believe that the ability to widely share apps is just as crucial as the ease of development. 

There are three main ways to deploy Streamlit apps: through a product created by Streamlit called Streamlit Sharing, through a cloud provider such as Amazon Web Services or Heroku, or through another product created by Streamlit called Streamlit for Teams...