Streamlit
Streamlit is an open-source Python library that allows developers to create and share beautiful, custom web apps for data science and machine learning in minutes. It provides a simple and easy-to-use API for building web applications, and it supports a wide range of data science and machine learning libraries, including Pandas, NumPy, and Scikit-learn. Streamlit also provides a number of features that make it easy to build and deploy web applications, including automatic UI generation, real-time updates, and support for a wide range of data formats. With Streamlit, developers can focus on writing code and building applications, rather than worrying about the details of web development. Streamlit is designed to be highly customizable, and it provides a number of features that make it easy to tailor the look and feel of web applications to meet the needs of specific users and use cases. Overall, Streamlit is a powerful and flexible tool for building web applications, and it has a wide range of potential use cases in data science, machine learning, and other fields. Streamlit is also highly extensible, and it provides a number of APIs and tools that make it easy for developers to add new features and functionality to the library. This makes it an ideal choice for developers who want to build custom web applications, and it has a wide range of potential use cases in data science, machine learning, and other fields. Streamlit also has a large and active community of developers, which means that there are many resources available for learning and troubleshooting, including documentation, tutorials, and forums. This makes it an ideal choice for developers who are new to web development, as well as those who are experienced and looking for a powerful and flexible tool for building web applications.
- Category
- Dev Tools
- Pricing
- Open Source
Problem solved
Streamlit solves the problem of building and deploying web applications for data science and machine learning. Many data scientists and machine learning engineers struggle with building and deploying web applications, as it requires a significant amount of expertise in web development. Streamlit provides a simple and easy-to-use API for building web applications, which makes it easy for data scientists and machine learning engineers to build and deploy web applications without having to learn a lot about web development. This saves time and effort, and it allows data scientists and machine learning engineers to focus on their core work of building and deploying models, rather than worrying about the details of web development.
Use cases
- Building and deploying web applications for data science and machine learning
- Creating custom web applications for data visualization and exploration
- Building and deploying web applications for machine learning model deployment and serving
- Creating custom web applications for data science and machine learning education and training
- Building and deploying web applications for data science and machine learning research and development
Audience
- Developers
- Data Scientists
- ML Engineers
Pros
- Easy to use and highly customizable
- Supports a wide range of data science and machine learning libraries
- Provides automatic UI generation and real-time updates
- Highly extensible and flexible
- Large and active community of developers
Cons
- Limited support for non-Python libraries and frameworks
- Can be slow for very large and complex applications
- Limited support for advanced web development features
Official website: https://github.com/streamlit/streamlit
Canonical LambdaBase page: https://www.lambdabase.com/tools/streamlit