What you’ll take away.
- Frame OpenShift AI components and responsibilities.
- Structure model and data workflows reproducibly.
- Assess deployment, monitoring and platform risks.
Your training in detail
- Who is it for?
- OpenShift administrators, data scientists, ML engineers and platform teams responsible for hybrid-cloud environments.
- Prerequisites
- Practical experience with containers, Kubernetes or OpenShift, plus foundations in machine learning and Python.
- Format & planning
- Indicative format: 4 days with a platform lab. The specific OpenShift AI version is confirmed before training.
Agenda: from understanding to application
Our independently developed agenda proposal. Focus areas, lab environment and scope are agreed before booking.
OpenShift AI architecture, projects, identity and resources
Data access, workbenches and reproducible development environments
Pipelines, experiments and model tracking
Model serving, generative AI workloads and secure interfaces
Monitoring, troubleshooting, version alignment and operational handover
Plan an isolated model workflow from development to serving. Document access, resources, tests, monitoring and a safe teardown.
Exams, licences & credentials
The course can be oriented around the published EX267 objectives. It does not replace the official Red Hat course or external exam; either is included only when confirmed in writing.
Vendor references & review date
Sources checked on 2026-10-06. These sources describe the technology or external exam requirements. They do not imply a partnership with NextNowa.
Your learning content
These objectives describe the training topic. The specific syllabus, duration, dates, trainer and price are agreed before booking.