AI296 – Develop, Test, and Run Granite Family LLMs with Red Hat Enterprise Linux AI

AI296 – Develop, Test, and Run Granite Family LLMs with Red Hat Enterprise Linux AI develops practical capability in Red Hat Enterprise Linux AI, Granite models, model serving, inference, data preparation, prompt design, tuning concepts, evaluation, testing, security, resource management, observability, and operational delivery.

The learning journey progresses through RHEL AI Architecture, Granite Models, Runtime, Hardware, and Environment Setup; Data Preparation, Prompt Design, Model Interaction, Inference, and Applications; Tuning Concepts, Evaluation, Testing, Safety, Security, and Quality Controls; Serving, Performance, Observability, Troubleshooting, Governance, and Operations. Each stage connects Red Hat concepts with implementation decisions, security, validation, troubleshooting, and support responsibilities.

Learners work through an AI engineering team developing and validating a Granite-based language-model application on RHEL AI and produce a practical RHEL AI project, model-serving configuration, evaluation results, security checklist, and operations guide suitable for implementation review and operational handover.

What You Will Learn

• Explain RHEL AI and Granite model architecture within a realistic Granite LLMs on RHEL AI implementation.
• Configure AI runtime and hardware preparation using clear prerequisites and repeatable Red Hat procedures.
• Apply data and prompt preparation to a production-focused Granite LLMs on RHEL AI scenario.
• Validate model inference and application integration with observable evidence and acceptance criteria.
• Troubleshoot tuning and evaluation concepts through targeted checks and corrective testing.
• Compare testing, safety, and security options and justify the selected approach.
• Document model serving and performance for review, support, and operational handover.
• Improve AI observability and operations through monitoring, automation, and governance.

SkillDynamo Training Approach

Training combines course-specific demonstrations, guided Red Hat exercises, scenario analysis, validation checklists, targeted troubleshooting, and a final improvement plan for Granite LLMs on RHEL AI.