Machine learning and data science code is easy to share but hard to use. GitHub overflows with models, algorithms, and datasets. But code is static. 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. Frustrated by this, we decided that we need a simple, sharable “play” button for machine learning code.

There are two challenges here. The first is creating apps that make data science and machine learning code interactive. The second is sharing these apps so that the world can experience your work.

A year ago, we addressed the first challenge — **creating — **by releasing Streamlit, an open-source library that lets you transform Python scripts into interactive apps. Streamlit lets you easily demonstrate algorithms, play with models, manipulate data, and combine all of these superpowers into beautiful apps. The response has been tremendous. We just crossed our millionth download. Hundreds of thousands of Streamlit apps have been created all over the world. But creating great apps only solves half the problem.

#deployment #data-science #streamlit #machine-learning