Developing and Deploying AI/ML Applications on Red Hat OpenShift AI (AI267) — Lesson 1: OpenShift AI Architecture, Projects, Workbenches, Notebooks, and Data Connections

Learning Objectives

• Interpret the architecture, dependencies, and expected behavior of OpenShift AI architecture, data science projects, and workbenches.
• Apply notebooks and data connections while preserving reviewable technical evidence.
• Recognize and correct the risk where a hidden prerequisite causes the first configuration step to produce an incomplete result.

Detailed Topics

• OpenShift AI architecture: 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.
• data science projects: 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.
• workbenches: 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.
• notebooks: 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.
• data connections: 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 openshift ai architecture, projects, workbenches, notebooks, and data connections by tracing the relevant architecture, commands, configuration, messages, and evidence. The walkthrough highlights how a hidden prerequisite causes the first configuration step to produce an incomplete result and shows how to isolate the cause without masking dependent issues.

Hands-On Exercise

Build a baseline and capture the initial state for OpenShift AI Architecture, Projects, Workbenches, Notebooks, and Data Connections. 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 OpenShift AI architecture in the stated scenario.
• Compare two approaches for notebooks 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 architecture, access, dependencies, and starting conditions separately, then correlate logs, events, commands, and configuration history. Do not accept the initial assumption that a hidden prerequisite causes the first configuration step to produce an incomplete result; confirm the cause with a reproducible retest.

Validation Checklist

• The required architecture, access, dependencies, and starting conditions 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 OpenShift AI Architecture, Projects, Workbenches, Notebooks, and Data Connections, 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.