DevOps & Infrastructure Focus: Step-by-Step AWS UPI Deployment, Custom MachineSets, and Auto-Scaling Infrastructure
What you'll learn
⚡ Set up core AWS infrastructure, IAM credentials, Route 53 DNS, and EC2 Bastion for OpenShift 4 UPI cluster deployments.
⚡ Customize install-config.yaml manifests, configure HA control planes, and manage AWS vCPU quota limits effectively.
⚡ Master OpenShift administration using the oc CLI, Web Console, kubeconfig, and inspect CRI-O container runtime health.
⚡ Scale cluster compute dynamically using MachineSets, MachineAutoscaler, and NVIDIA GPU Operators for AI/ML workloads.
⚡ Deploy Helm charts and K-Work (KWOAK) to simulate virtual GPU nodes for zero-cost Red Hat OpenShift AI (RHOAI) testing.
⚡ Understand core OpenShift platform abstractions including Pods, Projects, Console Routes, MachineSets, and millicores.
Requirements
❗ Basic understanding of Linux command-line operations, basic cloud concepts (AWS), and a free Red Hat & AWS account to follow along with the hands-on labs.
Description
Master Red Hat OpenShift 4 Infrastructure, AWS Integration, and Enterprise AI Scaling!
Are you ready to build, manage, and scale production-grade OpenShift clusters on AWS? Welcome to the ultimate hands-on guide designed for Cloud Engineers, DevOps Professionals, and MLOps Specialists. This course takes you step-by-step through deploying OpenShift 4 using theUser Provisioned Infrastructure (UPI) method-giving you total control over your enterprise cloud environment.
From day one, you will get your hands dirty with real-world cloud architecture. You will learn how to configure an AWS EC2 Bastion host, set up IAM programmatic access, and automate domain delegation using AWS Route 53 and GoDaddy DNS. Moving into cluster deployment, we dive deep into customizing install-config.yaml manifests, establishing high-availability (HA) control planes, and avoiding critical AWS vCPU quota limit pitfalls.
Once your cluster is live, you'll master daily cluster administration using both theOpenShift Web Console and theoc CLI. You will inspect nodes, debug CRI-O container runtime health, analyze resource allocations (millicores and memory), and demystify core platform concepts like Projects, Routes, and MachineSets.
Finally, we unlock advanced AI/ML infrastructure engineering. You will learn how to scale worker nodes dynamically usingMachineSets and MachineAutoscalers, handle real NVIDIA GPU instance types (G4dn series), and deploy Helm charts. Plus, you'll discover how to leverageK-Work (KWOAK) to simulate virtual GPU nodes forzero-cost AI and LLM inference testing before spending a dime in production!
What Makes This Course Unique?
✨100% Hands-On & Practical: No fluff-just real terminal commands, custom YAML configurations, and active AWS deployments.
✨Production-Ready UPI Method: Learn the exact deployment strategy used by enterprise architects for maximum security and flexibility.
✨Cost-Effective AI Infrastructure: Master virtual GPU simulation to learn OpenShift AI (RHOAI) workloads without incurring massive cloud bills.
Whether you're looking to upgrade your DevOps skills, prep for enterprise OpenShift deployments, or dive into MLOps infrastructure, this course provides the exact roadmap you need.Enroll today and take your OpenShift and AWS skills to the next level!
Who this course is for
⭐ DevOps Engineers, Cloud Architects, System Administrators, and MLOps Engineers looking to deploy and scale production-ready OpenShift clusters on AWS for AI/ML workloads.


https://rapidgator.net/file/dd96545480533185dd5572b3f6180979/OpenShift_Enterprise_Cluster_Masterclass_Deploy,_Scale,_Run.part2.rar.html,_Scale,_Run.part1.rar.html
https://rapidgator.net/file/a65ee818ae5283d0686718858b66bcac/OpenShift_Enterprise_Cluster_Masterclass_Deploy,_Scale,_Run.part1.rar.html,_Scale,_Run.part2.rar.html