Build and deploy machine learning web app using PyCaret and Streamlit

In our last post on deploying a machine learning pipeline in the cloud, we demonstrated how to develop a machine learning pipeline in PyCaret, containerize Flask app with Docker and deploy serverless using AWS Fargate. If you haven't heard about PyCaret before, you can read this announcement to learn more. In this tutorial, we will train a machine learning pipeline using PyCaret and create a web app using a Streamlit open-source framework. This web app will be a simple interface for business users to generate predictions on a new dataset using a trained machine learning pipeline. By the end of this tutorial, you will be able to build a fully functional web app to generate online predictions (one-by-one) and predictions by batch (by uploading a csv file) using trained machine learning model.