In this tutorial, we will be assuming the following:
- You have a working Streamlit app ready to deploy
- If you don't, no worries! The streamlit docs have some great tutorials,
but if you'd rather jump right in, you can go ahead and
git clone
my small example here.
- If you don't, no worries! The streamlit docs have some great tutorials,
but if you'd rather jump right in, you can go ahead and
- You have Docker installed
- You have a working knowledge of the command line
Streamlit is the framework featured in this post as it is designed for data scientists and machine learning engineers to quickly elevate their analysis from their laptops to deployment. Building useful, aesthetically-pleasing web applications is a diffult thing to do and Streamlit has taken great strides in enabling analysts with little web development experience to "create beautiful data apps in hours, not weeks."