Introduction to the Course

Get a brief introduction to the course, its intended audience, and prerequisites.

We'll cover the following

Overview

In any data science pipeline, building a prototype is typically the first step. It is critical to demonstrate machine learning models to the business so that they will be on board with the project and provide the support needed to implement models into production. As a data scientist, we need to be able to rapidly prototype models and build a minimum viable product.

There are currently many libraries in Python that aim to streamline the prototyping step by making it easy to wrap machine learning models in a user interface that can be deployed. Some of the more popular libraries include Flask, Streamlit, and, of course, Gradio. Each of these has its advantages and disadvantages. Flask is the most customizable and powerful library. However, it is also the most difficult to get started. This makes it slower to develop a quick prototype and perhaps more suited for creating powerful web applications. Streamlit and Gradio are similar and can typically achieve the same things quite easily. However, Gradio focuses more on rapidly building machine learning demos, whereas Streamlit has more customizations for visualizations. A comparison of these Python libraries is shown below.

Comparison of Popular Frameworks

Feature

Gradio

Streamlit

Flask

Primary Use Case

ML demos

Dashboards

Web interfaces

Ease of Use

It is easy to use, with a focus on providing a simple interface for users to input values and receive predictions from ML models.

It is easy to use, with a simple syntax for creating UI elements, charts, and plots. It is well-documented with good community support.

It requires knowledge of Flask framework and skills in HTML/CSS/JavaScript.

Rapid Prototyping

It enables quick prototyping—a few lines of code to set up a basic prototype. It focuses more on making ML prototyping easier.

It enables quick prototyping. It focuses more on making dashboarding easier.

It requires significantly more code to set up UI development.

Web Development

It abstracts away complexities of any web development.

It abstracts away complexities of any web development.

It gives full control over web development, but requires extensive development.

Deployment

It simplifies deployment process with various options. It can even launch within a Jupyter Notebook.

It allows simple deployment.

It requires manual configuration and setup.

In this course, we will learn how to use Gradio to build machine learning applications. We will be able to build a prototype and demo for machine learning applications quickly. This will prove useful in career progression; whether it is for rapidly building model interfaces for key stakeholders in the business or even for deploying personal projects to enhance data science portfolios.

Prerequisites

Since the course uses the Python library Gradio, a basic level of Python is required, but no knowledge of Gradio is needed.

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Intended audience

This course is suitable for those in the industry or academia looking to rapidly build demos of machine learning models or leverage existing models to build a minimum viable product.

This course will cover the following goals:

  • Understand the building blocks needed to build demos using Gradio

  • Build dashboards and visualizations with Gradio

  • Deploy machine learning models with a front-end user interface

  • Build chatbots with Gradio

  • Integrate powerful models and APIs from Hugging Face into demos