Fraud Detection Using OpenShift Data Science - A Step-by-Step Guide

Explore a comprehensive AI/ML use case for detecting fraudulent transactions in financial institutions using Red Hat OpenShift Data Science (RHODS). Learn about the solution's flow, integration with partner ecosystems like Starburst, and follow a detailed step-by-step guide to set up and implement the fraud detection workflow. Discover how to create a data science project, utilize the query editor, clean and scale data, and perform necessary configurations and testing. Gain valuable insights into leveraging RHODS for effective fraud detection in a 45-minute tutorial that covers everything from introduction to final testing.

Introduction
Example
OpenShift Data Science
Create Data Science Project
Requirements
Query Editor
Clean Data Set
Writer Scaling
Clean Data
Check Clean Data
Download Clean Data
Smoke
Guide
Configuration
Testing