Developing and Deploying AI/ML Applications on Red Hat OpenShift AI (AI267)
Build practical capability in OpenShift AI workbenches, notebooks, data science projects, model development, pipelines, model serving, data connections, monitoring, governance, and production deployment through four guided Red Hat lessons with exercises, validation, troubleshooting, and operational handover.
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-by SkillDynamo
- 0 Students
- Last updated
Course Description
Developing and Deploying AI/ML Applications on Red Hat OpenShift AI (AI267) develops practical capability in OpenShift AI workbenches, notebooks, data science projects, model development, pipelines, model serving, data connections, monitoring, governance, and production deployment.
The course progresses through OpenShift AI Architecture, Projects, Workbenches, Notebooks, and Data Connections; Model Development, Environments, Experiments, Reproducibility, and Collaboration; Data Science Pipelines, Artifacts, Automation, Validation, and Governance; Model Serving, Inference, Scaling, Monitoring, Security, and Production Delivery. Each lesson connects Red Hat technology with implementation choices, security, validation, troubleshooting, and support responsibilities.
Learners work through a data science and platform team moving an experimental model into a governed OpenShift AI production workflow and produce a practical OpenShift AI project, reproducible pipeline, model-serving deployment, monitoring evidence, and governance checklist that can support implementation, review, and operational handover.
What You Will Learn
- Explain OpenShift AI project and workbench setup as applied to OpenShift AI application delivery in realistic Red Hat environments.
- Configure notebook and data connection management for OpenShift AI application delivery using clear prerequisites and repeatable steps.
- Apply reproducible model development to a production-focused OpenShift AI application delivery scenario.
- Validate experiment and environment control for OpenShift AI application delivery with observable evidence and acceptance criteria.
- Troubleshoot data science pipeline automation in OpenShift AI application delivery through targeted checks and corrective testing.
- Compare artifact validation and governance options for OpenShift AI application delivery and justify the selected approach.
- Document model serving and inference scaling for OpenShift AI application delivery review, support, and operational handover.
- Improve production monitoring and security in OpenShift AI application delivery through monitoring, automation, and governance.
SkillDynamo Training Approach
Training combines course-specific demonstrations, guided Red Hat exercises, scenario analysis, validation checklists, targeted troubleshooting, and a final improvement plan for OpenShift AI application delivery.
Curriculum
- 1 Section
- 4 Lessons
- 32 Hours
- Course Curriculum4 Items
- 1.1Developing and Deploying AI/ML Applications on Red Hat OpenShift AI (AI267) — Lesson 1: OpenShift AI Architecture, Projects, Workbenches, Notebooks, and Data Connections8 hours
- 1.2Developing and Deploying AI/ML Applications on Red Hat OpenShift AI (AI267) — Lesson 2: Model Development, Environments, Experiments, Reproducibility, and Collaboration8 hours
- 1.3Developing and Deploying AI/ML Applications on Red Hat OpenShift AI (AI267) — Lesson 3: Data Science Pipelines, Artifacts, Automation, Validation, and Governance8 hours
- 1.4Developing and Deploying AI/ML Applications on Red Hat OpenShift AI (AI267) — Lesson 4: Model Serving, Inference, Scaling, Monitoring, Security, and Production Delivery8 hours
SkillDynamo Expert Team
Instructor-led delivery tailored to Developing and Deploying AI/ML Applications on Red Hat OpenShift AI (AI267), with Red Hat-specific demonstrations, guided exercises, scenario analysis, validation checklists, troubleshooting practice, and operational handover focused on OpenShift AI application delivery.
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