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Streamlit launches open source machine learning application development framework

Streamlit, a new machine learning startup from industry veterans, who worked at GoogleX and Zoox, launched today with a $6 million seed investment and a flexible new open source tool to make it easier for machine learning engineers to create custom applications to interact with the data in their models.
The seed round was led by Gradient Ventures with participation from Bloomberg Beta. A who’s who of solo investors also participated including Color Genomics co-founder Elad Gil, #Angels founder Jana Messerschmidt, Y Combinator partner Daniel Gross, Docker co-founder Solomon Hykes and Insight Data Science CEO Jake Klamka.
As for the product, Streamlit co-founder Adrien Treuille, says as machine learning engineers he and his co-founders were in a unique position to understand the needs of engineers and build a tool to meet their requirements. Rather than building a one-size-fits-all tool, the key was developing a solution that was flexible enough to serve multiple requirements, depending on the nature of the data the person is working with.
“I think that Streamlit actually has, I would say, a unique position in this market. While most companies are basically trying to systemize some part of the machine learning workflow, we’re giving engineers these sort of Lego blocks to build whatever they want,” Treuille explained.
Streamlit launches open source machine learning application development framework
Customized self-driving car data application built with Streamlit that enables machine learning engineers to interact with the data.
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