Gradio vs Streamlit vs Dash vs Flask

Gradio vs Streamlit vs Dash vs Flask

In this article, we’re going to look at 4 alternatives that you can use to deploy a machine learning model: Gradio, Streamlit, Dash, and Flask.

Comparing several web UI tools for data science!

Introduction

Machine learning models are exciting and powerful, but they aren’t very useful by themselves. Once a model is complete, it likely has to be deployed before it can deliver any sort of value. As well, being able to deploy a preliminary model or a prototype to get feedback from other stakeholders is extremely useful.

Recently, there has been an emergence of several tools that Data Scientists can use to quickly and easily deploy a machine learning model. In this article, we’re going to look at 4 alternatives that you can use to deploy a machine learning model: Gradio, Streamlit, Dash, and Flask.

Keep in mind that this is an opinionated article and is solely based off of my knowledge and experiences with these tools.

Summary: Which Should I Use?

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Gradio: Gradio is specifically built with machine learning models in mind. So if you want to create a web UI specifically for a machine learning model that you built, Gradio’s simple syntax and setup is the way to go.

Streamlit: Streamlit is useful if you want to get a dashboard up and running quickly, and have the flexibility to add lots of components and controls. As well, Streamlit allows you to build a web UI or a dashboard much faster than Dash or Flask.

Dash: Choose Dash if you want to be a production-ready dashboard for a larger company, since it’s mainly tailored for enterprise companies.

Flask: Choose Flask if you have knowledge of Python/HTML/CSS programming and you want to build your own solution completely from scratch.

data-science machine-learning python flask dash

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