Generative AI for IBM Power
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This course introduces generative AI concepts for IBM Power and explains how AI models, platforms, and infrastructure relate to Power-based environments. Learners examine use cases, deployment considerations, supporting technologies, and the role of IBM Power in enabling enterprise generative AI solutions.
Table of contents
- Short Summary
- Modality
- Cost
- Publish date
- Retire date
- Duration
- Agenda
- Objectives
- Brand/Department
- Product
- Skill Level
- Key Topics
- Audience
- Labs
- Follow-on Courses
Short Summary
This course introduces generative AI concepts and demonstrates how AI applications can be deployed and optimized on IBM Power servers using videos, hands-on labs and examples.
Modality
Self-paced
Cost
paid
Amount
300
Publish Date
December 9, 2025
Retire Date
January 30, 2029
Duration
16 hours
Agenda
- 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
Objectives
- 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