AI267: Developing and Deploying AI/ML Applications on Red Hat OpenShift AI

Module 1: Introduction to Red Hat OpenShift AI

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

Module 2: Data Science Projects

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

Module 3: Jupyter Notebooks

• Use Jupyter notebooks to execute and test code interactively

Module 4: Red Hat OpenShift AI Installation

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

Module 5: User and Resource Management

• Manage Red Hat OpenShift AI users and allocate resources

Module 6: Custom Notebook Images

• Create and import custom notebook images in Red Hat OpenShift AI

Module 7: Introduction to Machine Learning

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

Module 8: Training Models

• Train models by using default and custom workbenches

Module 9: Enhancing Model Training with RHOAI

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

Module 10: Model Serving in Red Hat OpenShift AI

• Serve trained machine learning models with OpenShift AI

Module 11: Introduction to Data Science Pipelines

• Define and set up Data Science Pipelines

Module 12: Working with Pipelines

• Create data science pipelines with the Kubeflow SDK and Elyra

Module 13: Controlling Pipelines and Experiments

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