Implementing a demonstration DL web environment

We will now take a deep dive into building a sample production application that uses online learning on the backend. We will be creating an application that can predict heart diseases, based on the Cleveland dataset. We will then deploy this model to Heroku, which is a cloud container-based service. Finally, we will demonstrate the online learning feature of the application.

You can find out more about Heroku by going to https://heroku.com.

Let's list the steps that we will be covering:

1. Build a predictive model on Jupyter Notebook.
2. Build a backend for the web application that predicts on the saved model.
3. Build a frontend for the web application that invokes incremental learning on the model.
4. Update the model on the server side incrementally.
5. Deploy the application to Heroku.

We will begin with the zeroth step; that is, observing...