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

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

To ensure you have a fulfilling learning experience with the AI267 course, it’s important to meet the following minimum prerequisites:

• Experience with Git: Familiarity with version control and source code management using Git.
• Experience in Python development: Understanding Python programming or completion of the Python Programming with Red Hat (AD141) course.
• Experience in Red Hat OpenShift: Basic user experience with Red Hat OpenShift or completion of the Red Hat OpenShift Developer II: Building and Deploying Cloud-native Applications (DO288) course.
• Basic understanding of AI, Data Science, and Machine Learning: A foundational knowledge in the fields of AI, data science, and machine learning concepts.

These prerequisites will help you get the most out of our comprehensive Red Hat OpenShift AI (AI267) course.

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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