Identify the main features of Red Hat OpenShift AI, and describe the architecture and components of Red Hat AI.

Organize code and configuration by using data science projects, workbenches, and data connections

Use Jupyter notebooks to execute and test code interactively

Install Red Hat OpenShift AI and manage Red Hat OpenShift AI components

Manage Red Hat OpenShift AI users and allocate resources

Create and import custom notebook images in Red Hat OpenShift AI

Describe basic machine learning concepts, different types of machine learning, and machine learning workflows

Train models by using default and custom workbenches

Use RHOAI to apply best practices in machine learning and data science

Describe the concepts and components required to export, share and  serve trained machine learning models

Serve trained machine learning models with OpenShift AI

Define and set up Data Science Pipelines

Create data science pipelines with the Kubeflow SDK and Elyra

Configure, monitor, and track pipelines with artifacts, metrics, and experiments