Popular machine learning and data science app framework Streamlit last week announced its version 1.0 milestone. Founded by machine learning veterans just two years ago, Streamlit was created to make it easy for data scientists to build web apps to explore and showcase data-centric projects, machine learning models, and complex data types. As reported by TechCrunch, Streamlit went from an early user base of a handful of customers to a significant community of users, driven in part by the popularity of Python. According to co-founder and chief operating officer Amanda Kelly, even users with a smattering of Python ability can get started with Streamlit to "quickly get an answer." "We launched Streamlit in 2019 with a radical idea: making data apps should be simple. Your response exceeded our highest hopes. Tens of thousands of data scientists and thousands of companies turned to Streamlit to share rich models, deep analyses, and complex datasets," said and co-founder Adrien Treuille in a blog post.

As a company founded by data scientists, Streamlit may be in a unique position to develop tooling to help companies build machine learning applications. For starters, it developed an open-source project, but today the startup announced an expanded beta of a new commercial offering and $35 million in Series B funding. Sequoia led the investment with help from previous investors Gradient Ventures and GGV Capital. Today's round brings the total raised to $62 million, according to the company. Data scientists can download the open-source project and build a machine learning application, but it requires a certain level of technical aptitude to make all the parts work.

AI and machine learning framework developer Streamlit today announced it raised $21 million in a financing round co-led by Gradient Ventures (Google's AI-focused investment arm) and GGV Capital. The company anticipates spending the bulk of the proceeds on expansion and product development, and on laying the runway for the launch of its enterprise-oriented Streamlit for Teams offering. Launching machine learning projects into production often requires stitching together internal tools. These tools can be difficult to deploy, require reasoning about client-server architecture, and don't integrate well with existing constructs and platforms. Moreover, they require frequent maintenance on the backend, which dedicated teams are sometimes too overwhelmed to provide.

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.