Developing and Deploying AI/ML Applications on Red Hat OpenShift AI (AI267) — Lesson 3: Data Science Pipelines, Artifacts, Automation, Validation, and Governance

Learning Objectives

• Evaluate security, resilience, integration, and exception handling for pipelines, artifacts, and automation.
• Apply validation and governance while preserving reviewable technical evidence.
• Recognize and correct the risk where the design works normally but fails under scale, dependency loss, policy enforcement, or integration delay.

Detailed Topics

• pipelines: apply the concept to a data science and platform team moving an experimental model into a governed OpenShift AI production workflow, record the relevant settings or decisions, and explain the operational consequence.
• artifacts: apply the concept to a data science and platform team moving an experimental model into a governed OpenShift AI production workflow, record the relevant settings or decisions, and explain the operational consequence.
• automation: apply the concept to a data science and platform team moving an experimental model into a governed OpenShift AI production workflow, record the relevant settings or decisions, and explain the operational consequence.
• validation: apply the concept to a data science and platform team moving an experimental model into a governed OpenShift AI production workflow, record the relevant settings or decisions, and explain the operational consequence.
• governance: apply the concept to a data science and platform team moving an experimental model into a governed OpenShift AI production workflow, record the relevant settings or decisions, and explain the operational consequence.

Instructor Demonstration

The instructor demonstrates data science pipelines, artifacts, automation, validation, and governance by tracing the relevant architecture, commands, configuration, messages, and evidence. The walkthrough highlights how the design works normally but fails under scale, dependency loss, policy enforcement, or integration delay and shows how to isolate the cause without masking dependent issues.

Hands-On Exercise

Introduce a controlled boundary condition and test recovery for Data Science Pipelines, Artifacts, Automation, Validation, and Governance. Add verified outcomes to the OpenShift AI project, reproducible pipeline, model-serving deployment, monitoring evidence, and governance checklist.

1. Confirm the scope, versions, access, dependencies, and success criteria.
2. Perform the workflow while recording commands, settings, output, and platform events.
3. Test one expected case and one controlled exception specific to this lesson.
4. Retest the correction and document ownership, rollback, and follow-up actions.

Scenario Analysis

• Identify which dependency most strongly influences pipelines in the stated scenario.
• Compare two approaches for validation and explain the security, reliability, and support trade-offs.
• Decide what evidence must be retained in the OpenShift AI project, reproducible pipeline, model-serving deployment, monitoring evidence, and governance checklist before operational acceptance.

Troubleshooting Guidance

Begin with the first observable difference from the expected result. Check security, integration, boundary conditions, and resilience separately, then correlate logs, events, commands, and configuration history. Do not accept the initial assumption that the design works normally but fails under scale, dependency loss, policy enforcement, or integration delay; confirm the cause with a reproducible retest.

Validation Checklist

• The required security, integration, boundary conditions, and resilience were checked and recorded.
• The normal path and a course-specific exception were tested.
• The OpenShift AI project, reproducible pipeline, model-serving deployment, monitoring evidence, and governance checklist links conclusions to commands, configuration, logs, or other objective evidence.
• Corrective actions, rollback steps, ownership, and handover notes are complete.

Lesson Outcome

Learners can apply Data Science Pipelines, Artifacts, Automation, Validation, and Governance, explain the observed Red Hat platform behavior, and support the result with a reviewable OpenShift AI project, reproducible pipeline, model-serving deployment, monitoring evidence, and governance checklist.