AI296 – Develop, Test, and Run Granite Family LLMs with Red Hat Enterprise Linux AI — Lesson 3: Tuning Concepts, Evaluation, Testing, Safety, Security, and Quality Controls

Learning Objectives

• Evaluate integration, security, resilience, performance, and exceptions affecting tuning concepts, model evaluation, and testing.
• Apply safety and security and quality while preserving reviewable technical evidence.
• Recognize and correct the condition where the implementation succeeds normally but fails during dependency loss, scale, policy enforcement, or recovery.

Detailed Topics

• tuning concepts: apply the concept to the course scenario, identify dependencies and controls, and record objective evidence of the result.
• model evaluation: apply the concept to the course scenario, identify dependencies and controls, and record objective evidence of the result.
• testing: apply the concept to the course scenario, identify dependencies and controls, and record objective evidence of the result.
• safety: apply the concept to the course scenario, identify dependencies and controls, and record objective evidence of the result.
• security and quality: apply the concept to the course scenario, identify dependencies and controls, and record objective evidence of the result.

Instructor Demonstration

The instructor demonstrates tuning concepts, evaluation, testing, safety, security, and quality controls by tracing architecture, commands, configuration, system messages, and evidence. The walkthrough tests how the implementation succeeds normally but fails during dependency loss, scale, policy enforcement, or recovery and explains how to isolate the cause without masking related issues.

Hands-On Exercise

Introduce a realistic boundary condition and test corrective behavior for Tuning Concepts, Evaluation, Testing, Safety, Security, and Quality Controls and add the verified result to the RHEL AI project, model-serving configuration, evaluation results, security checklist, and operations guide.

1. Confirm scope, versions, access, dependencies, assumptions, and acceptance criteria.
2. Perform the workflow while recording commands, configuration, output, events, and decisions.
3. Test the normal path and one course-specific exception or boundary condition.
4. Retest the correction and document rollback, ownership, and follow-up actions.

Scenario Analysis

• Identify which dependency most strongly influences tuning concepts in the stated scenario.
• Compare two approaches for safety and explain the security, reliability, performance, and support trade-offs.
• Decide what evidence must be retained in the RHEL AI project, model-serving configuration, evaluation results, security checklist, and operations guide before operational acceptance.

Troubleshooting Guidance

Begin with the first observable difference from the expected result. Check integration, security, boundary conditions, resilience, and performance separately, then correlate logs, events, commands, metrics, and configuration history. Treat the assumption that the implementation succeeds normally but fails during dependency loss, scale, policy enforcement, or recovery as a hypothesis until a reproducible retest confirms the cause.

Validation Checklist

• The required integration, security, boundary conditions, resilience, and performance were checked and recorded.
• The normal path and a course-specific exception were tested.
• The RHEL AI project, model-serving configuration, evaluation results, security checklist, and operations guide links conclusions to commands, configuration, logs, metrics, or other objective evidence.
• Corrective actions, rollback, ownership, and handover notes are complete.

Lesson Outcome

Learners can apply Tuning Concepts, Evaluation, Testing, Safety, Security, and Quality Controls, explain the observed platform behavior, and support the result with a reviewable RHEL AI project, model-serving configuration, evaluation results, security checklist, and operations guide.