Streamlit wants to revolutionize building machine learning and data science applications, scores $21 million Series A funding
Streamlit wants to be for data science what business intelligence tools have been for databases: A quick way to get to results, without bothering much with the details
We were confused at first when we got the news. We interpreted “application framework for machine learning and data science” to mean some new framework for working with data, such as PyTorch, DeepLearning4j, and Neuton, to name just a few among many others out there.
So, our first reaction was: Another one, how is it different? Truth is, Streamlit is not a framework for working with data per se. Rather, it is a framework for building data-driven applications. That makes it different to boot with, and there’s more.
Streamlit is aimed at people who don’t necessarily know or care much about application development: Data scientists. It was created by a rock star team of data scientists who met in 2013 while working at Google X, it’s open source, and has been spreading like wildfire, counting some 200.000 applications built since late 2019.
Today Streamlit announced that it has secured $21 million in Series A funding. ZDNet connected with CEO Adrien Treuille to discuss what makes Streamlit special, and where it, and data-driven applications at large, are going next.