Get started with the Fraud Detection workshop
Understand the tutorial scope
Welcome! In this workshop, you learn how to use data science, artificial intelligence (AI), and machine learning (ML) in an OpenShift development workflow.
You complete the following tasks in Red Hat OpenShift AI without installing any software 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 distributed computing frameworks.
-
Implement machine learning features to ensure consistency between training and inference.
Understand the 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 an earlier credit card transaction.
-
The price of the current transaction, compared to the median price of all the 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.
Prerequisites
You must have access to an OpenShift cluster which has Red Hat OpenShift AI installed.
|
If your cluster uses self-signed certificates, before you begin the workshop, your OpenShift AI administrator must add self-signed certificates for OpenShift AI as described in the Working with certificates documentation. |
If you’re ready, start the workshop.