A Practical Tutorial on Building Machine Learning Demos with Gradio


In this workshop, we will cover how to build machine learning web applications using the Gradio (www.gradio.dev) library.

First, we will start by walking through Gradio's core concepts, Interface and Blocks, and show how they can be used to create responsive machine learning web applications.

Then we will apply these core concepts to build real-time leaderboards and dashboards.

Finally, we will learn about Hugging Face Hub, covering how to find the right models and datasets for your machine learning tasks and deploy your machine learning web application so that anyone can use it.

Building machine learning demos and web apps has traditionally required significant knowledge of web development (css, js) and web hosting. We will discuss the Gradio library (www.gradio.dev), an alternative that allows you to build machine learning demos entirely in Python. This tutorial will be hands-on: we'll be going through a colab notebook and end by hosting the demo on Hugging Face Spaces, so be ready to code!

Session Outline:

Lesson 1: Gradio 101
Familiarize yourself with the core concepts of Gradio, Interface and Blocks. At the end of this lesson, you will be able to comfortably create a web application and share it with others. All while never having to leave Google Colab or Jupiter notebook!

Lesson 2: Creating Dashboards
We will apply the foundation we learned in Lesson 1 to build a real time data dashboard with Gradio. At the end of this lesson, you should know how to connect to cloud-hosted databases from Gradio and use Gradio's plotting components to create sleek visualizations that refresh automatically

Lesson 3: Gradio + the 🤗 Hub
We will take our dashboard from Lesson 2 and show how you can host it permanently, for free!, on the HuggingFace Hub. We will also cover how you can build off of Gradio apps hosted on the Hub to create new applications!

Background Knowledge:

Basic familiarity with python and google colab.


Freddy Boulton started his career as a data scientist for Nielsen where he built predictive models of television viewing behavior to make television ratings more accurate. This gave him a first hand-view of one of the biggest challenges faced by industry data scientists - being able to easily communicate and share machine learning models with stakeholders. He is currently solving that problem by working on Gradio, an open-source python library that lets data scientists create fully interactive demos of machine learning models with just a few lines of code.

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