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Streamlit, a popular app framework for data science and machine learning, has reached its version 1.0 milestone. The open source project is curated by a company of the same name that offers a commercial service built on the platform. So far, the project has had more than 4.5 million GitHub downloads and is used by more than 10,000 organizations.
The framework fills a vital void between data scientists who want to develop a new analytics widget or app and the data engineering typically required to deploy these at scale. Data scientists can build web apps to access and explore machine-learning models, advanced algorithms, and complex data types without having to master back-end data engineering tasks.
Streamlit cofounder and CEO Adrien Treuille told VentureBeat that “the combination of the elegant simplicity of the Streamlit library and the fact that it is all in Python means developers can do things in hours that normally took weeks.”
Examples of this increased productivity boost include reducing data app development time from three and a half weeks to six hours or reducing 5,000 lines of JavaScript to 254 lines of Python in Streamlit, Treuille said.
The crowded landscape of data science apps
The San Francisco-based company joins a crowded landscape filled with dozens of DataOps tools that hope to streamline various aspects of AI, analytics, and machine-learning development. Treuille attributes the company’s quick growth to being able to fill the gap between data scientists’ tools for rapid exploration (Jupyter notebooks, for one example) and the complex technologies companies use to build robust internal tools (React and GraphQL), front-end interface (React and JavaScript), and data engineering tools (dbt and Spark). “This gap has been a huge pain point for companies and often means that rich data insights and models are siloed in the data team,” Treuille said.
The tools are used by everyone from data science students to large companies. The company is seeing the fastest growth in tech-focused enterprises with a large base of Python users and a need to rapidly experiment with new apps and analytics.
“Every company has the same problems with lots of data, lots of questions, and too little time to answer all of them,” Treuille said.
Improvements in v1.0 include faster app speed and responsiveness, improved customization, and support for statefulness. The company plans to enhance its widget library, improve the developer experience, and make it easier for data scientists to share code, components, apps, and answers next year in 2022.
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