Red Hat OpenShift AI Training (AI267)
Implement monitoring, logging, performance analysis, model health tracking, and observability.
Red Hat OpenShift AI Training (AI267)
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Module 1: Introduction to Red Hat OpenShift AI
Understand the fundamentals of AI, machine learning, MLOps, and the architecture of Red Hat OpenShift AI Training.
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Module 2: Setting Up the OpenShift AI Environment
Learn how to configure OpenShift AI workspaces, notebooks, development environments, and project resources.
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Module 3: AI & Machine Learning Workflows
Build complete AI pipelines, prepare datasets, train models, and evaluate machine learning performance.
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Module 4: Data Preparation & Model Development
Work with datasets, feature engineering, preprocessing, experimentation, and model optimization techniques.
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Module 5: Model Deployment on OpenShift
Deploy AI models as scalable services using Kubernetes and OpenShift AI.
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Module 6: MLOps Fundamentals
Learn version control, model lifecycle management, continuous deployment, monitoring, and automation.
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Module 7: Monitoring AI Applications
Implement monitoring, logging, performance analysis, model health tracking, and observability.
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Module 8: Scaling AI Workloads
Manage GPU resources, autoscaling, workload optimization, and enterprise AI deployments.
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Module 9: AI Security & Governance
Secure AI workloads using authentication, RBAC, compliance, and responsible AI practices.
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Module 10: Capstone Project & Exam Readiness
Develop, deploy, monitor, and manage an end-to-end AI application using Red Hat OpenShift AI.
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