The Best Way to Deploy Data Science Projects with Streamlit
Hint: It's not Streamlit Cloud
Impact.
It’s what separates good data scientists from great ones.
It’s what every employer is looking for in their next hire.
And it’s what defines your value as a data scientist.
But creating impact takes more than just technical skills. It’s not enough to build great models—you need to make them accessible and usable.
That’s why deployment is so important.
A model sitting in a Jupyter notebook isn’t doing anything. To create real value, you need to deploy it.
So today, I want to show you how to do just that using Streamlit
But going beyond the basic Streamlit Cloud.
This is the same method I’ve used to coach many of my mentees, helping them build projects that impress hiring managers—and get hired.
Here is what we’ll cover:
Why Streamlit Cloud isn’t enough
How to Dockerize your Streamlit app (practical guide, no fluff)
Deploying a Dockerized Streamlit app
Extra resources
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1. Why Streamlit Cloud Isn’t Enough
Nowadays using Streamlit to bring your portfolio projects to life is a must. It’s a great way to show your ability to build projects end-to-end.
Well, sort of.