Model Serving, Inference, Scaling, Monitoring, Security, and Production Delivery develops a focused part of Developing and Deploying AI/ML Applications on Red Hat OpenShift AI (AI267). The lesson uses a data science and platform team moving an experimental model into a governed OpenShift AI production workflow to connect Red Hat concepts with implementation, validation, security, and support decisions.
The instructor demonstrates model serving, inference, scaling, monitoring, security, and production delivery by tracing the relevant architecture, commands, configuration, messages, and evidence. The walkthrough highlights how the technical fix is successful but monitoring, rollback, ownership, or handover remains incomplete and shows how to isolate the cause without masking dependent issues.
Run an operational review and produce support-ready evidence for Model Serving, Inference, Scaling, Monitoring, Security, and Production Delivery. Add verified outcomes to the OpenShift AI project, reproducible pipeline, model-serving deployment, monitoring evidence, and governance checklist.
Begin with the first observable difference from the expected result. Check monitoring, troubleshooting, rollback, ownership, and handover separately, then correlate logs, events, commands, and configuration history. Do not accept the initial assumption that the technical fix is successful but monitoring, rollback, ownership, or handover remains incomplete; confirm the cause with a reproducible retest.
Learners can apply Model Serving, Inference, Scaling, Monitoring, Security, and Production Delivery, 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.