Easy deployment and public hosting with Streamlit and Streamlit Sharing

You’ve analyzed a data set and found interesting insights, you have built a machine learning pipeline, but so far they just live within your jupyter notebook. If others want to view your work they have to read through your notebook and view every output, that’s only ideal in a few cases. It’s time to take your work and showcase it interactively.

This does not have to be hard and you don’t require the help of a front-end developer. You, as a data scientist or someone on the path to becoming one, can deploy and host your project or application.

All you need is streamlit.

Streamlit allows you to create apps with a lot of functionality using just a few lines of code. You can host these apps locally or deploy them online. In the past, you had to use Heroku or a similar service to deploy your apps online, but now streamlit has its own sharing platform which makes deployment very easy.

I will start with a quick overview of some basic streamlit functions so you can begin building your application and then go over the process of hosting an app on streamlit sharing.

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