Today, we address the second challenge - sharing - by announcing a brand-new sharing platform for Streamlit. Easily deploy and share your Streamlit apps But creating great apps only solves half the problem. Hundreds of thousands of Streamlit apps have been created all over the world. Streamlit lets you easily demonstrate algorithms, play with models, manipulate data, and combine all of these superpowers into beautiful apps. The second is sharing these apps so that the world can experience your work.Ī year ago, we addressed the first challenge - creating - by releasing Streamlit, an open-source library that lets you transform Python scripts into interactive apps.
The first is creating apps that make data science and machine learning code interactive. Frustrated by this, we decided that we need a simple, sharable "play" button for machine learning code.
Can you play with the models? See the algorithms? Interact with the data? Doing so requires following complex instructions, installing packages, or reading dense code snippets. GitHub overflows with models, algorithms, and datasets. Machine learning and data science code is easy to share but hard to use.
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