Streamlit Now 10x Faster With New Update, Intuitive Features & More

Funnily enough, we feel like we are back where we started.
Streamlit Now 10x Faster With New Update, Intuitive Features & More

Two years ago, Streamlit launched its first version of an open-source app framework for machine learning and data science professionals, where they could easily build data apps with just a few lines of code. It helps in sharing rich models, deep analysis, and complex datasets seamlessly. 

Upping the notch, the company recently unveiled an updated version of Streamlit – Streamlit 1.0, which is not just easy to use but more powerful – 10 times faster than the previous version.

Build and Share Data Apps, Faster 

In its latest blog post, the company revealed its product journey on how it created one of the most powerful, production-ready app frameworks, Streamlit, while preserving its core simplicity. In just two lines of Python, Streamlit dissolves all complexity of app development – layout, input, output, interaction, and callbacks. 

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Streamlit is one of the fastest ways to build and share data apps. Once developed, the user can use its cloud platform to deploy, manage, and share their app. 

Check out the source code and examples of Streamlit on GitHub

Scripting Models At Scale 

Thanks to the community of Steamlit users, the company was able to make countless iterations and revisions to the APIs in a short span of time. Here’s what they did: 

  • Layouts: In October last year, Streamlit introduced new layout features – columns, expander and container, giving developers and data scientists more control over their app’s presentation and user interface. 
Streamlit Now 10x Faster With New Update, Intuitive Features & More
(Source: Streamlit

Check out all the components added on Streamlit here

  • State: In that same month, Streamlit worked on state, adding new complexity in a few lines of code. 
  • Speed: Last month, it introduced powerful new caching primitives that made apps faster. 

Enters Streamlit 1.0 

With these powerful new features and preserved Streamlit’s core simplicity, the latest version of Streamlit is all set to fly. Having grown from three co-founders to an almost 50-members team, the company is now expanding its team and actively hiring. 

The Streamlit community has grown significantly in the last two years, with close to 4.5 million downloads to date. On GitHub, the app framework has over 16,000 stars and is currently used by more than 10K organisations, including over half of Fortune 50 companies. 

What’s next? 

“Funnily enough, we feel like we are back where we started. Once again, our community wants powerful features. But this time, it is different. This time we know that the Streamlit model will scale,” said Adrien Treuille, co-founder and CEO at Streamlit, in his blog post. 

Sharing the roadmap, Treuille said that Streamlit would continue to make magical apps, curate first-class developer experience, enhance user experience and rapidly expand the ecosystem. 

Here’s what you can expect from Streamlit in the coming years. 

Magical apps: Streamlit will add an unbeatable set of widgets – everything from sortable/filterable/editable databases and tables to clickable charts, image selectors and editors, audio and video players and uploaders. 

First-class developer experience: Streamlit looks to make it easier for developers to connect to data sources, cache data, interact with it, and debug their apps. 

Enhanced UX: Streamlit will be designing a different user experience, where app users can understand the app, interact with it, and give users direct feedback. 

Expand ecosystem: Streamlit will be launching a new feature that makes it super simple to get started with apps, find code snippets, search for the right add-on components, engage with the community, and get recognised for their contributions. 

Last but not least, Streamlit is committing to follow a loose variant of semantic versioning.

Amit Raja Naik
Amit Raja Naik is a seasoned technology journalist who covers everything from data science to machine learning and artificial intelligence for Analytics India Magazine, where he examines the trends, challenges, ideas, and transformations across the industry.

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