Content By Devops .com
SAN FRANCISCO, April 07, 2021 (GLOBE NEWSWIRE) — Streamlit, the creators of the fastest and most powerful app framework for machine learning and data science, today formally introduced Streamlit for Teams, the company’s first commercial product. Streamlit for Teams lets data scientists instantly deploy and share apps with teammates, clients and other stakeholders so they can make rapid, data-informed decisions based on the insight from the apps. As of today, the new Teams product is moving out of developer preview into beta before launching later this year.
Streamlit is an open source, powerful and easy-to-use framework, first introduced in 2019, that lets data scientists quickly build web apps to access and explore machine learning models, advanced algorithms and complex data types. These apps are everything from advanced analytics dashboards to sales and marketing tools based off of the latest predictive algorithms. Streamlit’s unique workflow is 10x faster than other alternatives, making it possible for data scientists to go from idea to deployed app in only a few hours. Streamlit has more than 14,000 GitHub stars, has been downloaded nearly two million times and is used by hundreds of companies, including 7-Eleven, Apple, Ford and Uber.
“Streamlit apps are simple interactive script visualizations – a deceptively powerful idiom that strikes just the right balance between low code, power and customizability. This unique approach enables such fast creation of powerful, useful apps, that Streamlit apps have become an entirely new workflow within companies — similar to Google Docs and Notion. Streamlit for Teams lets companies instantly bring these apps into the entire company, allowing everyone to make faster, data-informed decisions,” said Adrien Treuille, co-founder and CEO of Streamlit.
With the introduction of Streamlit for Teams, Streamlit is unlocking the ability for data scientists to instantly deploy and share Streamlit apps with teammates, clients and other stakeholders. Streamlit for Teams is a zero-effort cloud platform to securely deploy, manage, debug and collaborate on apps. The product deploys apps directly from private Git repos and runs continuous integration to instantly update apps on commits. It layers on enterprise-grade data security and OAuth2-based authentication as well as advanced collaboration features for both data scientists and their customers.
“I can’t say enough great things. Streamlit for Teams is so slick and intuitive. It lets us deploy apps that our company loves and that even helps us close deals with new clients,” said Kyle Long, engineer at PocketList, a rental platform company.
Streamlit Raises $35 Million in Series B Growth Funding Round
Today Streamlit also announced $35 million in Series B funding, bringing the total raised to $62 million. The round was led by Sequoia and previous investors Gradient Ventures and GGV Capital also participated. Streamlit will use this money to continue to scale its team, expand its platform and bring its technology to leading enterprises.
Sonya Huang, partner at Sequoia and Streamlit board member, said: “The field of data is changing rapidly. Static dashboards are no longer the best way for data scientists to communicate insights and democratize data access. Interactive, data-rich web apps are the future. We believe Streamlit has a unique opportunity to disrupt the $25 billion Business Intelligence market with its open source and developer-first approach—ultimately, becoming a core piece of the data science and machine learning stack for years to come. We are thrilled to partner with this exceptional team as Streamlit continues to experience wide adoption within the data science community and in the Fortune 1000.”
Streamlit makes creating web apps from Python fast and easy. Data scientists are able to go from data and models to deployed apps in a matter of hours and only a handful of lines of code. Streamlit is backed by Sequoia, Gradient Ventures and GGV Capital. For more information, visit http://streamlit.io or follow @streamlit.