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Chapter 1. Introduction

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

You use an example fraud detection model to complete the following tasks in Red Hat OpenShift AI without the need to install anything on your computer:

  • 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.
  • Learn how to train the model by using Ray, a distributed computing framework.

1.1. About the example fraud detection model
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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, by entering a PIN number, or by making an online purchase.

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

1.2. Before you begin
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You should have access to an OpenShift cluster where Red Hat OpenShift AI is installed.

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

Important

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 (Self-Managed) or Working with certificates (Cloud Service).

If you’re ready, start the tutorial.

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