Streamlit, which helps data scientists build apps, hits version 1.0

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Streamlit, a popular app framework for computer science and machine learning, has reached its milestone in version 1.0. The open source project is curated by a company of the same name that offers a commercial service built on the platform. To date, the project has had more than 4.5 million GitHub downloads and is used by more than 10,000 organizations.

The framework fills an important gap between computer scientists who want to develop a new analysis widget or app and computer technology that is typically required to implement these on a large scale. Data researchers can build web apps to access and explore machine learning models, advanced algorithms and complex data types without having to master back-end computer engineering tasks.

Streamlit co-founder and CEO Adrien Treuille told VentureBeat that “the combination of the sleek simplicity of the Streamlit library and the fact that it’s all in Python means developers can do things in hours that normally took weeks.”

Examples of this increased productivity increase 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 with data science apps

The San Francisco-based company joins a crowded landscape filled with dozens of DataOps tools hoping to streamline various aspects of AI, analytics and machine-learning development. Treuille attributes the company’s rapid growth to being able to fill the gap between data researchers’ tools for rapid investigation (Jupyter notebooks, for example) and the complex technologies companies use to build robust internal tools (React and GraphQL), front-end interface (React and JavaScript) and computer 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 emptied into the data team,” Treuille said.

The tools are used by everyone from computer science students to large companies. The company is experiencing the fastest growth in tech-focused companies with a large number of Python users and a need to quickly experiment with new apps and analytics.

“All companies have the same problems with lots of data, lots of questions and too little time to answer them all,” Treuille said.

Improvements in v1.0 include faster app speed and responsiveness, improved customization, and statefulness support. The company plans to improve its widget library, enhance the developer experience and make it easier for data researchers to share code, components, apps and answers next year in 2022.

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