Opleiding: Generative AI for IBM Power [QZC52DG]

OVERVIEW

The goal of this course is to provide the student with a tangible understanding and a real hands-on experience with generative AI applications deployed and optimized for IBM Power systems. This self-paced virtual course uses video lectures, review questions, and virtual lab machine exercises to provide the student with foundational knowledge and experience about the topics covered in the course. In the lecture, the student begins by learning the basics of generative AI, and the basic components of a generative AI application, then applies these concepts to real-world examples on Power where the student learns Power offerings for AI workloads, package management fundamentals in Python, Operating System deployment strategies on Power, and Power hardware use cases.

The lab exercises will start with viewing and manipulating files in a basic AI application running on IBM Power Red Hat Enterprise Linux. They will have hands-on experience identifying the components of that AI application and their connection to other components, and will add functionalities to the application that demonstrate more advanced AI application techniques like frameworks, prompt tuning, and conversation memory. Then, the students will go through the process of setting up a Python virtual environment, investigating Power hardware resources, and using the Hardware Management Console (HMC) to ensure memory and other resources are optimized for AI workloads.

Updated 22/04/2026

OBJECTIVES

In this course participants will learn:

• Understand foundational genAI concepts and terms
• Familiarize with the larger genAI ecosystem for AI applications
• Distinguish between basic genAI math and hardware terms
• Understand IBM Power's current offerings for AI workloads
• Set up package channels and repositories for AI libraries on Power
• Identify AI application components in an example implementation of AI inferencing on Power
• Describe Power-specific optimizations and recommendations for GenAI
• Refer to peripheral applications and services that help to develop AI apps on Power

CONTENT

Unit 0: Introduction

Video 0-1: Introduction

Unit 1: Generative AI applications

Video 1-1: Generative AI concepts

Video 1-2: Inferencing

Video 1-3: Code

Unit 1 review questions

Exercise 1: Generative AI applications

EX01 Section 1: Working through a Jupyter Notebook of an AI application

EX01 Section 2: Adding and managing advanced generative AI app features

EX01 Section 3: Adding conversation memory to a generative AI application

EX01 Section 4: Implementing an AI framework

Unit 2: Math, hardware, and Power offerings for generative AI

Video 2-1: Math and hardware terms

Video 2-2: Power offerings for AI

Unit 2 review questions

Unit 3: Implementing generative AI on Power

Video 3-1: Package management

Video 3-2: Retrieval-augmented generation

Video 3-3: Implementing genAI on Power

Unit 3 review questions

Unit 4: Deploying AI on Power

Video 4-1: Performance considerations

Video 4-2: AI deployment options

Unit 4 review questions

Exercise 2: Deploying AI on Power Red Hat Enterprise Linux (RHEL)

EX02 Section 1: Viewing a virtual environment and installed packages

EX02 Section 2: Viewing hardware resources

EX02 Section 3: Creating a virtual environment and investigating HMC hardware resources