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

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:Installing Red Hat OpenShift AI

• Installing Red Hat OpenShift AI by using the web console and the CLI, and managing Red Hat OpenShift AI components

Module 5:Managing Users and Resources

• Managing Red Hat OpenShift AI users, and resource allocation for Workbenches

Module 6:Custom Notebook Images

• Creating custom notebook images, and importing a custom notebook through the Red Hat OpenShift AI dashboard

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:Introduction to Model Serving

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

Module 11:Model Serving in Red Hat OpenShift AI

• Serve trained machine learning models with OpenShift AI

Module 12:Custom Model Servers

• Deploy and serve machine learning models by using custom model serving runtimes

Module 13:Introduction to Data Science Pipelines

• Create, run, manage, and troubleshoot data science pipelines

Module 14:Elyra Pipelines

• Creating a Data Science Pipeline with Elyra

Module 15:KubeFlow Pipelines

• Creating a Data Science Pipeline with KubeFlow SDK