Deploying Streamlit apps using Streamlit sharing

Deploying Streamlit apps using Streamlit sharing

A sneak peek into Streamlit’s new deployment platform. I’ve been playing around with a new Streamlit feature called Streamlit sharing, which makes it super easy to deploy your custom apps.

Over the past couple of weeks, I’ve been playing around with a new Streamlit feature called Streamlit sharing, which makes it super easy to deploy your custom apps. I’m going to go through a bit of background first, so if you want to see the docs for Streamlit sharing to get started you can find them here.

Streamlit background

For a bit of background, Streamlit is a framework that lets you quickly and confidently turn a python script into a web app and is an incredible tool for data scientists working on teams where they need to quickly share a model or an interactive analysis, or for data scientists working on personal projects they want to show the world. Here’s a Streamlit beginner tutorial if you want to try it out!

I’ve been using Streamlit for the past ~6 months, and it’s been so useful. Previously, if I knew I wanted to make a web app at the end of a project, I would always opt to switch to R for the wonderful R shiny framework, even though I am a much better python programmer than an R one. Going through Django or flask is just so much development friction to take on that it’s rarely worth it for a personal project and always takes too long for anything at work. But after using Streamlit, I now not only had options but found myself preferring python+Streamlit to R+shiny.

data data-science streamlit python

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