Although strong and valuable, AI types are useless without a Web UI tool. A type will almost surely require to be built before it is of any use. 

Numerous tools have been developed that data scientists may use to quickly and easily build a machine learning or deep learning types. Although machine learning types are fascinating and strong, they aren't really helpful on their own. 

Before a model can offer any kind of value, it will probably require to be built once it is finished. Additionally, it is quite helpful to be able to use a rough model or prototype to gather input from other stakeholders.

Currently, several tools have become available that data scientists may employ to build a machine-learning model quickly and simply. We'll examine which will remain the best tool for you in this post.


A Python backend tool for delivering apps is called Flask. Almost any app may be deployed with Flask. You are not restricted to using applications that use data. 

Flask allows you to add components, but you may also easily create your own on the fly.

Pros of Flask:

  • Fully examined.
  • It is quite adaptable.
  • Extremely scalable

Cons of Flask:

  • Counting on how complex it is, building and deploying an app takes time.
  • You'll need to understand front-end development since this framework is solely for the backend.
  • HTML, CSS, and JavaScript proficiency, as well as intermediate Python knowledge, are required.

Streamlit - A Rundown:

Streamlit is an additional well-liked tool for designing user interfaces. It is an open-source framework for Python web development that can be used to build customized, original web applications for data science and machine learning. 

Streamlit is compatible with several well-known libraries and frameworks. Streamlit is a wonderful option if you need to set up your dashboard quickly and have the ability to add several components and features. Additionally, it can create web UI and dashboards more quickly than Dash or Flask.

Perks Of Streamlit:

  • Simple to learn.
  • Streamlit may be used by anybody familiar with Python. In no way are HTML and CSS necessary.
  • The development to deployment period is quite condensed.
  • Routing is not anything to be worried about.

Cons of Streamlit:

  • Changes to any front-end elements are challenging.
  • It cannot be scaled.
  • Just a few features remain in beta.

Flask vs. Streamlit:

Flask is a web framework, whereas Streamlit is a platform for data dashboarding. Data dashboards have an essential but minor component called page serving. 

Flask cannot visualize, manipulate, or analyze data, but as it's a standard Python library, it can coexist peacefully alongside other libraries that accomplish these things. An all-in-one technology called Streamlit includes both web serving and data analysis.

Flask is scalable and can be very easily tailored to your preferences. It has undergone extensive testing. In addition, the community has been quite supportive. The user must have experience with frontend programming, as Flask solely offers backend help, which might be intimidating at first.

For Streamlit, however, there is no need to worry about front-end development because it is simpler to understand and necessitates less time between the creation and deployment stages. However, it has certain downsides of its own, such as the lack of scale, the fact that it is still in development, and the fact that many Flask features are missing. Furthermore, it lacks Flask's extensive ecosystem of support and community.

If you require a structured data dashboard with many of the necessary components already included, consider Streamlit. If you don't want to reinvent the wheel and want to develop a data dashboard with reusable parts, use Streamlit.

If you have the technical ability and want to create a highly customized solution from scratch, use Flask.

Wrapping Up:

Flask and Streamlit have different purposes; Flask is a tool for building app backends and APIs, whereas Streamlit is a tool for quickly constructing apps with little code. As a solution, Flask will be more complicated than Streamlit. 

Aside from the two frameworks that have already been mentioned, several of these libraries are rather new. While some are adaptable and can fit into your system, some are more inflexible and have their structure.