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

Course Prerequisites

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

1. Introduction:
   The "Developing and Deploying AI/ML Applications on Red Hat OpenShift AI (AI267)" course equips IT professionals with essential skills to deploy and manage AI/ML applications on the Red Hat OpenShift platform.

   
   

2. Target Audience and Job Roles:

   
   

• Data Scientists
• Machine Learning Engineers
• DevOps Engineers
• Software Developers
• AI/ML Practitioners
• IT Professionals with Python and Git experience
• System Administrators managing AI/ML workflows
• Cloud-native Application Developers
• Red Hat OpenShift Administrators
• Research Scientists specializing in data science
• Technical Leads in AI/ML projects

Learning Objectives - What you will Learn in this Developing and Deploying AI/ML Applications on Red Hat OpenShift AI (AI267)?

Introduction to Learning Outcomes

The Developing and Deploying AI/ML Applications on Red Hat OpenShift AI (AI267) course provides fundamental knowledge and hands-on experience in developing, training, and deploying AI/ML models using Red Hat OpenShift AI.

Learning Objectives and Outcomes

• Introduction to Red Hat OpenShift AI

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

• Data Science Projects

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

• Jupyter Notebooks

  • Utilize Jupyter notebooks to execute and test code interactively.

• Installing Red Hat OpenShift AI

  • Install Red Hat OpenShift AI using the web console and CLI, and manage its components.

• Managing Users and Resources

  • Manage users and allocate resources for workbenches in Red Hat OpenShift AI.

• Custom Notebook Images

  • Create and import custom notebook images through the Red Hat OpenShift AI dashboard.

• Introduction to Machine Learning

  • Understand basic machine learning concepts, types, and workflows.

• Training Models

  • Train models using default and custom workbenches.

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