Build and evaluate a fraud detection model with TensorFlow and ONNX

Learning path | 4 resources | 1 hr and 25 mins | Published on May 20, 2025
Alex Krikos Ramakrishna Yekulla Ian Lawson

Learn how to deploy a trained model with Red Hat OpenShift AI and use its capabilities to simplify environment management. By the end of this learning path, you'll have gained familiarity with managing and deploying your models effectively using OpenShift AI. We will use fraud detection as the example use case.

Try it in our Developer Sandbox

  1. Overview: Build and evaluate a fraud detection model with TensorFlow and ONNX
  2. Create and configure your workbench Page | 10 mins
  3. Configure and execute an experiment Page | 45 mins
  4. Train and verify your model Page | 30 mins

Overview: Build and evaluate a fraud detection model with TensorFlow and ONNX

TensorFlow-Onnx-LP-featured-image

In the world of machine learning (ML), managing trained models effectively is crucial. Red Hat OpenShift AI provides powerful tools to automate and streamline the ML lifecycle. This learning path delves into creating a project, training and testing a fraud model, and saving the model.
Our primary aim in this learning path is to thoroughly log all activities within OpenShift AI. Let's step through the implementation.

Prerequisites:

In this learning path, you will:

  • Create a notebook.
  • Launch a notebook.
  • Make predictions from a trained model.
  • Load a dataset.
  • Build a model.
  • Convert a model to ONNX.
  • Test a model.