Chapter 1. Introduction


In this tutorial, you learn how to incorporate data science and artificial intelligence and machine learning (AI/ML) into an OpenShift development workflow.

You will use an example fraud detection model to complete the following tasks:

  • Explore a pre-trained fraud detection model by using a Jupyter notebook.
  • Deploy the model by using OpenShift AI model serving.
  • Refine and train the model by using automated pipelines.

And you do not have to install anything on your own computer, thanks to Red Hat OpenShift AI.

1.1. About the example fraud detection model

The example fraud detection model monitors credit card transactions for potential fraudulent activity. It analyzes the following credit card transaction details:

  • The geographical distance from the previous credit card transaction.
  • The price of the current transaction, compared to the median price of all the user’s transactions.
  • Whether the user completed the transaction by using the hardware chip in the credit card, entered a PIN number, or for an online purchase.

Based on this data, the model outputs the likelihood of the transaction being fraudulent.

1.2. Before you begin

If you don’t already have an instance of Red Hat OpenShift AI, see the Red Hat OpenShift AI page on the Red Hat Developer website for information on setting up your environment. There, you can create an account and access the free OpenShift AI Sandbox or you can learn how to install OpenShift AI on your own OpenShift cluster.


If your cluster uses self-signed certificates, before you begin the tutorial, your OpenShift AI administrator must add self-signed certificates for OpenShift AI as described in Working with certificates.

If you’re ready, start the tutorial!