If you're building a custom app for data science and machine learning, Streamlit is the best way to do it. Streamlit is an open-source framework for data scientists and machine learning (ML) engineers. Generally, data experts and ML engineers aren't web developers. Therefore, they don't always have the skills to build finished apps. Their work and focus lie elsewhere, creating no desire to spend weeks learning how to use the traditional frameworks often utilized to build web apps. Transform your data: experience the power of a programmable spreadsheet! Explore now!

Streamlit is an easy-to-understand alternative to other options, allowing those without developer skills to create great apps with only a few lines of code. The framework enables you to deploy models easily using a programmable spreadsheet for efficient data exploration and analysis. It's also efficient for ML model deployment and development. Best of all, you don't need a good grasp of web development basics to get started.

Understanding Streamlit

As a Python-based library, Streamlit is a surprisingly easy framework to work with. In addition to handling many backend complexities, Streamlit enables first-time developers to create eye-catching and intuitive user interfaces. There is no front-end experience or knowledge required.

Streamlit is also compatible with other major Python libraries like pandas, seaborn, Keras, etc. From a performance standpoint, the framework simplifies data caching. The result is faster and more powerful computation pipelines.

There are many potential uses of Streamlit.

It's the go-to for ML engineers looking to build apps for end users with zero coding knowledge. Create a slick user interface, and users can run machine learning models without using confusing lines of code. It's also a production-ready framework and is the fastest way to create apps at all development stages.

Streamlit is also a powerful tool for data scientists. Developers can add a programmable spreadsheet to Streamlit apps directly. Doing so allows data scientists to import, clean and transform datasets into the right format. The framework also makes it easier to interact with data, visualize massive datasets and generate dashboards. These capabilities are possible with significantly fewer lines of code than other frameworks, making Streamlit more accessible to first-time developers.

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Emily Clarke writes about business software and services like spreadsheets that automatically generate Python code and transform your data with AI etc. You can find her thoughts at spreadsheet automation blog.