Developing and Deploying AI/ML Applications on Red Hat OpenShift AI with Exam (AI268)

Target group

• Data scientists and AI practitioners who want to use Red Hat OpenShift AI to build and train ML models
• Developers who want to build and integrate AI/ML enabled applications
• MLOps engineers responsible for installing, configuring, deploying, and monitoring AI/ML applications on Red Hat OpenShift AI

Course structure

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

Data Science Projects

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

Jupyter Notebooks

Use Jupyter notebooks to execute and test code interactively

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

Managing Users and Resources

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

Custom Notebook Images

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

Introduction to Machine Learning

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

Training Models

Train models by using default and custom workbenches

Enhancing Model Training with RHOAI

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

Introduction to Model Serving

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

Model Serving in Red Hat OpenShift AI

Serve trained machine learning models with OpenShift AI

Introduction to Data Science Pipelines

Create, run, manage, and troubleshoot data science pipelines

Elyra Pipelines

Create data science pipelines with Elyra

Kubeflow Pipelines

Create data science pipelines with Kubeflow Pipelines