Udemy
    •  
    •  
    •  
    •  
    •  
    •  
    •  
    •  
Subscribe
Turn what you know into an opportunity and reach millions around the world.
Learn More
Your cart is empty.
Keep shopping
OpenShift Enterprise Cluster Masterclass: Deploy, Scale, Run
New

OpenShift Enterprise Cluster Masterclass: Deploy, Scale, Run

DevOps & Infrastructure Focus: Step-by-Step AWS UPI Deployment, Custom MachineSets, and Auto-Scaling Infrastructure
New
Created byVimal Daga
Last updated 7/2026
English

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.

Explore related topics

Course content

2 sections • 5 lectures • 2h 31m total length

  • OpenShift 4 AWS Setup: EC2 Bastion, IAM Credentials & Route 53 DNS Configuration37:33

    OpenShift 4 AWS Setup: EC2 Bastion, IAM Credentials & Route 53 DNS Configuration

    In this foundational lesson of our Red Hat OpenShift AI series, we prepare the core AWS cloud environment for a custom User Provisioned Infrastructure (UPI) multi-node deployment.

    What you will learn in this video:

    - Launching an AWS EC2 Bastion/Installer instance with Amazon Linux.

    - Configuring programmatic access using AWS CLI, IAM Policies, and Access Keys.

    - Downloading RHOCP installer binaries and cluster pull secrets directly from the Red Hat Hybrid Cloud Console.

    - Setting up public DNS domain delegation using AWS Route 53 and GoDaddy Name Servers.

    This essential setup lays the groundwork for provisioning production-ready OpenShift clusters optimized for enterprise DevOps, MLOps, and AI workloads.



  • Deploying Custom OpenShift UPI Clusters: Install-Config Customize & Quota Limits35:43

    Deploying Custom OpenShift UPI Clusters: Install-Config Customization & AWS Quota Limits

    Dive deep into configuring and launching your OpenShift 4 multi-node cluster using the User Provisioned Infrastructure (UPI) method on AWS.

    Key topics covered in this lecture:

    - Generating and customizing the `install-config.yaml` file for custom instance types and worker nodes.

    - Configuring high availability (HA) control planes and dedicated compute node topologies.

    - Understanding AWS VPC networking, overlay drivers, and default instance requirements.

    - Identifying and resolving critical AWS Service Quota limits (vCPU, On-Demand instances, EC2 limits) to prevent deployment failures.

    By the end of this video, your OpenShift installer will initiate cluster creation with custom configurations tailored to your workload needs.


  • OpenShift Cluster Management: Web Console Access, OC CLI Setup & Core Concepts25:26

    OpenShift Cluster Management: Web Console Access, OC CLI Setup & Core Concepts

    Once your cluster deployment completes successfully, learn how to authenticate, manage, and inspect your OpenShift platform using both GUI and CLI interfaces.

    Key takeaways from this video:

    - Accessing the OpenShift Web Console and exporting admin credentials (`kubeconfig`).

    - Installing and configuring the OpenShift Client (`oc` CLI) on Linux.

    - Inspecting node resources, millicores, memory usage, and CRI-O container runtime health.

    - Demystifying core OpenShift concepts: Pods, Namespaces vs. Projects, Console Routes, and MachineSets.

    Gain total visibility over your cluster compute nodes and master the internal control mechanisms that drive OpenShift AI environments.

  • Advanced OpenShift Scaling: MachineSets, AutoScaler & Simulated GPU Nodes45:11

    Advanced OpenShift Scaling: MachineSets, AutoScaler & Simulated GPU Nodes (K-Work)

    Learn how to scale compute resources on demand and integrate GPU acceleration for machine learning workloads without incurring prohibitive cloud costs.

    Highlights of this session:

    - Scaling worker nodes manually and declaratively using OpenShift MachineSets.

    - Configuring MachineAutoscaler YAML manifests for dynamic horizontal node scaling.

    - Provisions for real NVIDIA GPU instance types (G4dn series) and handling GPU quota requests.

    - Deploying Helm charts, NVIDIA GPU Operators, and K-Work (KWOAK) to simulate virtual GPU nodes for zero-cost AI testing.

    This masterclass video prepares your infrastructure for full Red Hat OpenShift AI (RHOAI) deployment, model training, and LLM inference pipelines.


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 the User 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 the OpenShift Web Console and the oc 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 using MachineSets and MachineAutoscalers, handle real NVIDIA GPU instance types (G4dn series), and deploy Helm charts. Plus, you’ll discover how to leverage K-Work (KWOAK) to simulate virtual GPU nodes for zero-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.

Report abuse