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

An introduction to developing and deploying AI/ML applications on Red Hat OpenShift AI.

Developing and Deploying AI/ML Applications on Red Hat OpenShift AI (AI267) provides students with the fundamental knowledge about using Red Hat OpenShift for developing and deploying AI/ML applications. This course helps students build core skills for using Red Hat OpenShift AI to train, develop and deploy machine learning models through hands-on experience.

This course is based on Red Hat OpenShift 4.14, and Red Hat OpenShift AI 2.8.
Note: This course is offered as a 3 day in person class, a 4 day virtual class or is self-paced. Durations may vary based on the delivery. For full course details, scheduling, and pricing, select your location then get started on the right hand menu.

Course Content Summary

• Introduction to Red Hat OpenShift AI
• Data Science Projects
• Jupyter Notebooks
• Installing Red Hat OpenShift AI
• Managing Users and Resources
• Custom Notebook Images
• Introduction to Machine Learning
• Training Models
• Enhancing Model Training with RHOAI
• Introduction to Model Serving
• Model Serving in Red Hat OpenShift AI
• Introduction to Workflow Automation
• Elyra Pipelines
• KubeFlow Pipelines

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

• Experience with Git is required
• Experience in Python development is required, or completion of the Python Programming with Red Hat (AD141) course
• Experience in Red Hat OpenShift is required, or completion of the Red Hat OpenShift Developer II: Building and Deploying Cloud-native Applications (DO288) course
• Basic experience in the AI, data science, and machine learning fields is recommended

Technology considerations

• No ILT classroom will be available

• 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