Chapter 5. Next steps

OpenShift AI tutorial - Fraud detection example

Step-by-step guidance to complete the following tasks with an example fraud detection model:

Working in your data science IDE

Learn how to access your workbench IDE (JupyterLab, code-server, or RStudio Server).

For the JupyterLab IDE, learn about the following tasks:

Working with data science pipelines

Enhance your data science projects on OpenShift AI by building portable machine learning (ML) workflows with data science pipelines, by using Docker containers. Use pipelines for continuous retraining and updating of a model based on newly received data.

Serving models

Deploy your ML models on your OpenShift cluster to test and then integrate them into intelligent applications. When you deploy a model, it is available as a service that you can access by using API calls. You can return predictions based on data inputs that you provide through API calls.

Serving models

The Red Hat OpenShift AI service includes model deployment options for hosting the model on Red Hat OpenShift Dedicated or Red Hat Openshift Service on AWS for integration into an external application.

Working with accelerators

If you work with large data sets, you can use accelerators, such as NVIDIA GPUs, AMD GPUs, and Intel Gaudi AI accelerators, to optimize the performance of your data science models in OpenShift AI. With accelerators, you can scale your work, reduce latency, and increase productivity.

Working with distributed workloads

Implement distributed workloads to use multiple cluster nodes in parallel for faster, more efficient data processing and model training.

Working with connected applications

Extend your core OpenShift AI solution with integrated third-party applications. Several leading AI/ML software technology partners, including Starburst, Intel AI Tools, and IBM are also available through Red Hat Marketplace.