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OVERVIEW In this course you will learn how to create a new Citrix Virtual Apps and Desktops deployment. Get hands-on as the course guides you through the architecture, communications, management, installation, and configuration of Citrix Virtual Apps and Desktops to host apps and desktops for your users. This course is a necessary step in enabling you with the right training and skills, to not only understand, manage, and deliver successfully, but also to make well-informed planning decisions along the way. Product Versions Covered: Citrix Virtual Apps and Desktops 7 (2507 LTSR) for Citrix Private Cloud, Universal Hybrid Multi-Cloud, and Citrix Platform customers OBJECTIVES Understand the architecture and communication flows for Citrix Virtual Apps and Desktops 2507 LTSR. Install, configure, and manage Citrix Virtual Apps and Desktops environments. Create Citrix Virtual Apps and Desktops workloads. Deliver single-session and multi-session resources to users. Prepares learner for Citrix Certified Associate - Virtualization (CCA-V) exam. AUDIENCE For administrators and engineers CONTENT Module 1: Introduction to Citrix Virtual Apps and Desktops & Architecture Introduction to Citrix Virtual Apps and Desktops New Citrix Virtual Apps and Desktops Lifecycle Citrix Virtual Apps and Desktops 2507 LTSR Refresh – What’s New Overview of Citrix Virtual Apps and Desktops Components Citrix Virtual Apps and Desktops Features and Resource Capabilities Hosting Platform Considerations: Architecture By Layers Connection Flow Process Introduction Module 2: Citrix Virtual Apps and Desktops Site Considerations Pre-Deployment Considerations Citrix Licensing Setup Delivery Controller Setup Citrix Virtual Apps and Desktops Site Setup Redundancy Considerations Module 3: Provisioning Workloads: VDA and Master Images Master Image Creation Methods Master Image Requirements  Optimizing VDAs – Citrix Optimizer Citrix Workspace Environment Management Overview Module 4: Provisioning Workloads: VDA and Master Image Prep Machine Catalogs and Delivery Groups Provisioning Methods and Considerations Machine Creation Services – Deep Dive MCS Environment Considerations Module 5: – Providing Access to End Users StoreFront Overview StoreFront Beacons Citrix Workspace App Deployment and Configuration  StoreFront Features Module 6: Published App and Desktop Presentation and Management Published App Properties Server OS Published App Optimizations Published App Presentation Application Groups Apps and Desktops Presentation Module 7: Managing the User Experience Citrix Policies – Methods to Manage Policies New Secure Defaults for VDA functionality Citrix HDX Features – Using Policies HDX Optimization Module 8: Printing for User Sessions Printing Concepts and Printer Provisioning Printer Drivers Print Environment - Considerations Module 9: Profile Management User Profiles – Introduction and Considerations Configuring Citrix Profile Management Profile Containers – VHDX-based Policies Module 10: Manage the Site Delegated Administration Use PowerShell with Citrix Virtual Apps and Desktops Power Management Considerations Using Autoscale to Power Manage Machine Module 11: Citrix Virtual Apps and Desktops Basic Security Considerations Citrix Admin Security Considerations XML Service Security Considerations Secure HDX External Traffic Module 12: Manage Citrix Virtual Apps and Desktops with Director Citrix Director Introduction Monitor and Interact with User Sessions Published Apps Analysis Monitor the Machines Running the VDA Site Specific Common Monitoring Alerts and Notifications Optimize Citrix Director Monitoring with Citrix Application Delivery Management Module 13: Manage - Supporting and Troubleshooting Citrix Virtual Apps and Desktops Workloads Introduction to Supporting a Citrix Virtual Apps and Desktops Site A List of Common Tools Common Tasks for Proactive Administration

OVERVIEW The Implementing Cisco Data Center AI Infrastructure (DCAI) course is designed to equip professionals with the skills to support, secure, and optimize AI workloads within modern data center environments. This comprehensive program delves into the unique characteristics of AI/ML applications, their influence on infrastructure design, and best practices for automated provisioning. Participants will gain in-depth knowledge of security considerations for AI deployments and master day-2 operations, including monitoring and advanced troubleshooting techniques such as log correlation and telemetry analysis. Through hands-on experience, including practical application with tools like Splunk, learners will be prepared to efficiently monitor, diagnose, and resolve issues in AI/ML-enabled data centers, ensuring optimal uptime and performance for critical organizational workloads. This training prepares you for the 300-640 DCAI v1.0 exam. If passed, you earn the Cisco Certified Specialist - Data Center AI Infrastructure certification and satisfy the concentration exam requirement for the Cisco Certified Network Professional (CCNP) Data Center certification. This training is worth 38 Continuing Education (CE) Credits. OBJECTIVES After completing this course you should be able to: Describe key concepts in artificial intelligence, focusing on traditional AI, machine learning, and deep learning techniques and their applications  Describe generative AI, its challenges, and future trends, while examining the nuances between traditional and modern AI methodologies  Explain how AI enhances network management and security through intelligent automation, predictive analytics, and anomaly detection  Describe the key concepts, architecture, and basic management principles of AI-ML clusters, as well as describe the process of acquiring, fine-tuning, optimizing and using pre-trained ML models  Use the capabilities of Jupyter Lab and Generative AI to automate network operations, write Python code, and leverage AI models for enhanced productivity  Describe the essential components and considerations for setting up robust AI infrastructure  Evaluate and implement effective workload placement strategies and ensure interoperability within AI systems  Explore compliance standards, policies, and governance frameworks relevant to AI systems  Describe sustainable AI infrastructure practices, focusing on environmental and economic sustainability  Guide AI infrastructure decisions to optimize efficiency and cost  Describe key network challenges from the perspective of AI/ML application requirements  Describe the role of optical and copper technologies in enabling AI/ML data center workloads  Describe network connectivity models and network designs  Describe important Layer 2 and Layer 3 protocols for AI and fog computing for Distributed AI processing  Migrate AI workloads to dedicated AI network  Explain the mechanisms and operations of RDMA and RoCE protocols  Understand the architecture and features of high-performance Ethernet fabrics  Explain the network mechanisms and QoS tools needed for building high-performance, lossless RoCE networks  Describe ECN and PFC mechanisms, introduce Cisco Nexus Dashboard Insights for congestion monitoring, explore how different stages of AI/ML applications impact data center infrastructure, and vice versa  Introduce the basic steps, challenges, and techniques regarding the data preparation process  Use Cisco Nexus Dashboard Insights for monitoring AI/ML traffic flows  Describe the importance of AI-specific hardware in reducing training times and supporting the advanced processing requirements of AI tasks  Understand the compute hardware required to run AI/ML solutions  Understand existing intelligence and AI/ML solutions  Describe virtual infrastructure options and their considerations when deploying  Explain data storage strategies, storage protocols, and software-defined storage  Use NDFC to configure a fabric optimized for AI/ML workloads  Use locally hosted GPT models with RAG for network engineering tasks AUDIENCE Anyone looking to: Acquire comprehensive skills to support, secure, and optimize AI workloads within modern data center environments Understand the design, implementation, and advanced troubleshooting of AI infrastructure, including network challenges and specialized hardware Gain in-depth knowledge of AI/ML concepts, generative AI, and their practical application in network management and automation Apply hands-on techniques for monitoring, diagnosing, and resolving issues,leveragingtools like Splunk and utilizing AI for enhanced productivity in network operations Prepare for the 300-640 DCAI v1.0 exam CERTIFICATION Recommended as preparation for the following exam: 300-640 - DCAI - Implementing Cisco Data Center AI Infrastructure CONTENT Fundamentals of Al Introduction to Artificial Intelligence Traditional AI Traditional AI Process Flow Traditional AI Challenges Modern Applications of Traditional AI Machine Learning vs. Deep Learning ML vs. DL Techniques and Methodologies ML vs. DL Applications and Use Cases Generative Al Generative AI Generative Adversarial Frameworks GenAI Use Cases Generative AI Inference Challenges GenAI Challenges and Limitations GenAI Bias and Fairness GenAI Resource Optimization Generative AI vs. Traditional AI Future Trends in AI AI Language Models LLMs vs. SLMs Al Use Cases Analytics Network Optimization Network Automation and Self-Healing Networks Capacity Planning and Forecasting Cybersecurity Predictive Risk Management Threat Detection Incident Response Collaboration and Communication Internet of Things (IoT) Al-ML Clusters and Models AI-ML Compute Clusters AI-ML Cluster Use Cases Custom AI Models-Process Custom AI Models-Tools Prebuilt Al Model Optimization Pre-Trained AI Models AI Model Parameters Service Placements - On-Premises vs. Cloud vs. Distributed Al Toolset-Jupyter Notebook AI Toolset-Jupyter Notebook Al Infrastructure Traditional AI Infrastructure Modern AI Infrastructure Al Workloads Placement and Interoperability Workload Mobility Multi-Cloud Implementation Vendor Lock-In Risks Vendor Lock-In Mitigation Al Policies Data Sovereignty Compliance, Governance, and Regulations Al Sustainability Green AI vs. Red AI Cost Optimization AI Accelerators Power and Cooling Al Infrastructure Design Project Description Your Role Key Network Challenges and Requirements for Al Workloads Bandwidth and Latency Considerations Scalability Considerations Redundancy and Resiliency Considerations Visibility Nonblocking Lossless Fabric Congestion Management Considerations Al Transport Optical and Copper Cabling Organizing Data Center Cabling Ethernet Cables InfiniBand Cables Ethernet Connectivity InfiniBand Connectivity Hybrid Connectivity Connectivity Models Network Types: Isolated vs. Purpose-Built Network Network Architectures: Two-Tier vs. Three-Tier Hierarchical Model Networking Considerations: Single-Site vs. Multi-Site Network Architecture Al Network Layer 2 Protocols Layer 3 Protocols Scalability Considerations for Deploying AI Workloads Fog Computing for AI Distributed Processing Architecture Migration to AI/ML Network Project Description Your Role Application-Level Protocols RDMA Fundamentals RDMA Architecture RDMA Operations RDMA over Converged Ethernet High-Throughput Converged Fabrics InfiniBand-to-Ethernet Transition Cisco Nexus 9000 Series Switches Portfolio Building Lossless Fabrics Traditional QoS Toolset Enhanced Transmission Selection Intelligent Buffer Management on Cisco Nexus 9000 Series Switches AFD with ETRAP Dynamic Packet Prioritization Data Center Bridging Exchange Lossless Ethernet Fabric Using RoCEv2 Advanced Congestion Management with AFD Congestion Visibility Explicit Congestion Notification Priority Flow Control Congestion Visibility in AI/ML Cluster Networks Using Cisco Nexus Dashboard Insights Pipeline Considerations Data Preparation for Al Data Processing Workflow Overview Data Processing Workflow Phases AI/ML Workload Data Performance Use Cisco Nexus Dashboard Insights for monitoring AI/ML traffic flows Al-Enabling Hardware CPUs, GPUs, and DPUs GPU Overview NVIDIA GPUs for AI/ML Intel GPUs for AI/ML DPU Overview SmartNIC Overview Cisco Nexus SmartNIC Family NVIDIA BlueField SuperNIC Compute Resources Compute Hardware Overview Intel Xeon Scalable Processor Family Overview Cisco UCS C-Series Rack Servers Cisco UCS X-Series Modular System Mapping AI/ML Workloads to Cisco UCS Servers GPU Sharing Compute Resources Sharing Total Cost of Ownership AI/ML Clustering Compute Resource Solutions Cisco Hyperconverged Infrastructure Solutions Overview Cisco Hyperconverged Solution Components FlashStack Data Center Nutanix GPT-in-a-Box Run:ai on Cisco UCS Virtual Resources Virtual Infrastructure Device Virtualization Server Virtualization Defined Virtual Machine Hypervisor Container Engine Storage Virtualization Virtual Networks Virtual Infrastructure Deployment Options Hyperconverged Infrastructure HCI and Virtual Infrastructure Deployment Storage Resources Data Storage Strategy Fibre Channel and FCoE NVMe and NVMe over Fabrics Software-Defined Storage Setting Up Al Cluster Use NDFC to configure a fabric optimized for AI/ML workloads. Deploy and Use Open Source GPT Models for RAG Use locally-hosted GPT models with RAG for network engineering tasks. Al Infrastructure Operations and Monitoring The Need for AI Infrastructure Monitoring Monitoring Compute Monitoring Storage Monitoring the Runtime Layer Monitoring AI Fabrics The Need for Al Infrastructure Lifecycle Management Compute Lifecycle Upgrades Fabric Lifecycle Upgrades Troubleshooting Al Infrastructure Log Correlation for AI Applications Telemetry Analysis for AI Workloads Hands-On Telemetry for AI Workloads Timing Protocols Troubleshoot Common Issues in AI/ML Fabric Overview of Splunk Enterprise and Splunk Cloud Data Ingestion Methods Splunk Applications Basics of Splunk SPL

OVERVIEW The Automate AI Solutions on Cisco Infrastructure (DCAIAA) Learning Path empowers you to transcend traditional network management paradigms and embrace a future where intelligent automation drives efficiency, resilience, and security for your AI-powered infrastructure. The Learning Path delves into advanced network automation technologies, equipping you with the skills to orchestrate zero-touch provisioning (ZTP), learn about UI-based automation with templates, harness AI/ML templates in Cisco NDFC, implement AIOps for proactive infrastructure management, explore the potential of agentic AI, automate with infrastructure as code (IaC) tools, leverage Cisco Nexus-as-Code (NaC), and integrate Cisco Intersight for compute endpoint automation. This course is just over 7 hours in Duration OBJECTIVES After completing this course you should be able to: Design and implement automated network solutions that accelerate AI/ML deployments Optimize network performance for demanding AI/ML workloads Improve network security and compliance Reduce operational costs and improve efficiency Drive innovation and enable new AI-powered business initiatives   CONTENT AI Automation Pre-Assessment Automation and Configuration Principles Fabric Automation with IaC Tools Compute and Storage Automation Configure Cisco Nexus Switches with Ansible Post-Assessment

OVERVIEW The Operate and Troubleshoot AI Solutions on Cisco Infrastructure (DCAIAOT) course equips you with the skills to monitor and troubleshoot data center infrastructure—compute, storage, and network components supporting AI/ML workloads. Starting with foundational concepts, you’ll learn lifecycle management and explore key troubleshooting techniques like log correlation, telemetry analysis, and timing protocols. The Learning Path covers essential tools such as Splunk for telemetry and troubleshooting. A hands-on lab simulation lets you use Splunk Enterprise to resolve issues with an unresponsive AI application. After completing this Learning Path, you’ll be able to efficiently monitor, diagnose, and resolve issues in AI/ML-enabled data centers, ensuring reliable performance and maximum uptime for mission-critical workloads. This course, along with the AI Solutions on Cisco Infrastructure Essentials (DCAIE) course, prepares you for the Implementing Cisco Data Center AI Infrastructure (300-640 DCAI) exam. If passed, you earn the Cisco Certified Specialist - Data Center AI Infrastructure certification and fulfill the concentration exam requirement for the Cisco Certified Network Professional (CCNP) Data Center certification. This course is just under 4 hours in duration and is worth 4 Continuing Education Credits. OBJECTIVES After completing this course you should be able to: Monitor and troubleshoot compute, storage, and network components in AI/ML data centers Apply lifecycle management to data center infrastructure supporting AI/ML workloads Use log correlation and telemetry analysis for efficient problem diagnosis Understand and apply timing protocols for infrastructure troubleshooting Utilize Splunk and Splunk Enterprise for telemetry and issue resolution Diagnose and resolve unresponsive AI applications through hands-on lab simulations Ensure reliable performance and maximum uptime for mission-critical AI/ML workloads CONTENT Operate and Troubleshoot AI Solutions on Cisco Infrastructure Pre-Assessment AI Infrastructure Operations and Monitoring Troubleshooting AI Infrastructure Troubleshoot Common Issues in AI/ML Fabric Post-Assessment

OVERVIEW The Automating Cisco Data Center Networking Solutions (DCNAUTO) training teaches you how to implement and optimize automation in Cisco data center environments. You will gain hands-on experience with Cisco Nexus platforms, programmability features, and modern automation tools used to streamline operations across switching, compute, and fabric controllers. The training covers foundational concepts in network programmability, then advances into day-zero provisioning, on-box automation using Bash, Python, and Guest Shell, and off-box automation with Cisco NX-API, NETCONF/RESTCONF, and YANG models. You will also explore Infrastructure as Code (IaC) workflows with Cisco Nexus Dashboard Fabric Controller (NDFC), Ansible, and Terraform, as well as network validation and testing with Cisco pyATS. Finally, you will learn how AI-driven operations enhance network automation and simplify lifecycle management. This training prepares you for the 300-635 DCNAUTO exam. If passed, you earn the Cisco Certified Specialist - Data Center Networking Automation certification and satisfy the concentration exam requirements for the Cisco Certified Network Professional (CCNP) Data Center and Automation certifications. OBJECTIVES After taking this course, you should be able to: Explain the role of programmability and automation in Cisco data center networks Explain the benefits of programmability compared to manual CLI workflows Identify data models and data formats (XML, JSON, YAML) used in Cisco automation frameworks Use version control systems such as Git for storing and managing configuration files Perform day-zero provisioning on Cisco Nexus devices using Power-On Auto Provisioning (POAP) Enable and use the Bash shell and Guest Shell on Cisco Nexus devices Run Linux commands inside Guest Shell to interact with NX-OS and external services Write Python scripts on-box to parse CLI output and enhance operational workflows Describe and configure Cisco NX-API CLI and REST interfaces Send JSON/XML payloads to NX-API using Python scripts and verify device responses Use Cisco NX-API Developer Sandbox for testing and validation Implement model-driven programmability using NETCONF/RESTCONF and YANG data models Construct and validate Python scripts to configure and verify protocols with NX-OS APIs Implement off-box automation with Cisco NX-API CLI/REST, NETCONF/RESTCONF, and YANG models Describe Cisco NDFC architecture and automation capabilities Use NDFC REST APIs for fabric automation tasks Automate fabric provisioning and configuration with Ansible playbooks Build and apply Terraform plans for managing data center fabrics with NDFC Describe Cisco pyATS and Genie frameworks for network validation Build and run pyATS test cases to verify device state before and after automation Interpret test results and integrate them into automation workflows Describe how AI and ML capabilities are applied in Cisco Data Center automation Explain AI-driven monitoring and anomaly detection workflows Correlate AI insights with automated remediation actions AUDIENCE - Network designers - Systems engineers - Wireless engineers - Consulting systems engineers - Technical solutions architects - Network administrators - Wireless design engineers - Network managers - Site reliability engineers - Deployment engineers - Sales engineers - Account managers - Program managers - Project managers CERTIFICATION This training prepares you for the 300-635 DCNAUTO exam. If passed, you earn the Cisco Certified Specialist - Data Center Networking Automation certification and satisfy the concentration exam requirements for the Cisco Certified Network Professional (CCNP) Data Center and Automation certifications. Cisco Certified Network Professional (CCNP) Data Center and Automation certifications CONTENT OUTLINE Day-Zero Provisioning On-Box Automation with Cisco NX-OS Cisco Nexus Automation with NX-API CLI Cisco Nexus Programmability with NX-API REST Model-Driven Programmability on NX-OS IaC Tools IaC Lifecycle Cisco NX-OS Automation with IaC Tools Cisco ACI Automation with IaC Tools Cisco Nexus Dashboard Automation with IaC Tools Simulation of Data Center Topologies Network Change Validation with pyATS Model-Driven Telemetry Implementation Troubleshoot Infrastructure Automation Troubleshoot Container Workloads Connectivity AI-Assisted Coding AI Security Considerations AI Agent Integration LABS OUTLINE Set Up PowerOn Auto Provisioning on the Cisco Nexus 9000 Use Bash and Guest Shell on Cisco NX-OS Use Python to Enhance CLI Commands Make NX-API Calls with NX-API Sandbox Configure and Verify NX-OS Using Python Set Up API Calls with Bruno Use NX-API REST with Python Configure and Verify Using NETCONF, RESTCONF, and YANG Track Changes with Git and GitHub Use Ansible with Cisco NX-OS Use Terraform with Cisco NX-OS Generate Configuration Using Jinja2 Templates Manage ACI Configuration Using Ansible Set Up a New Tenant the NetDevOps Way Automate ACI with Terraform Automate NDFC with REST API and Python Retrieve NX-OS Health Data Using Cisco Nexus Dashboard Create NDFC Fabric with Ansible Automate NDFC with Terraform Explore Cisco Modeling Labs Basics Simulate Data Center Network with Cisco Modeling Labs Cisco ACI Simulator Installation and Initialization Simulation Capture and Compare Network State with pyATS CLI Run Network Tests Using pyATS and Python Configure a Subscription for Model-Driven Telemetry Troubleshoot Infrastructure as Code Troubleshoot Linux Container Connectivity AI Toolset—Jupyter Notebook Al-Driven Monitoring Using Nexus Dashboard Simulation

OVERVIEW The Designing Cisco UCS-X Infrastructure for AI Workloads (DCUCSX) Learning Path introduces you to the modular Cisco UCS X-Series platform, designed to meet the dynamic demands of AI, machine learning, big data, and virtual desktop workloads. You will learn how to leverage Cisco Intersight for cloud-based management, enabling simplified operations and enhanced scalability. This Learning Path guides you through architecture, hardware components, and software-defined capabilities that allow you to optimize infrastructure, reduce costs, and future-proof your data center. By understanding these technologies, you will be empowered to design and deploy AI-ready infrastructure that supports both traditional and next-generation workloads with efficiency and agility. This course is just over 9 hours in duration and is worth 9 Continuing Education Credits. OBJECTIVES After completing this course you should be able to: Understand the modular architecture and benefits of Cisco UCS X-Series for AI workloads Use Cisco Intersight to simplify infrastructure management and automate operations Design scalable, composable compute and GPU resources tailored to AI/ML demands Optimize network and storage fabrics for high-throughput AI training and inferencing Apply best practices for deploying, monitoring, and supporting AI infrastructure efficiently CONTENT Cisco UCS-X Infrastructure Design for AI Workloads Pre-Assessment Cisco UCS-X Architecture Cisco UCS AI Sizing Cisco UCS-X and GPU Solutions Implementation in Intersight Cisco UCS-X Operations and Support Automation for Cisco UCS-X Post-Assessment

OVERVIEW Create and manage virtual machines by using the Red Hat OpenShift Virtualization operator. Red Hat OpenShift Virtualization Administration I: Operating Virtual Machines teaches the essential skills required to create and manage virtual machines (VMs) on OpenShift by using the Red Hat OpenShift Virtualization operator. OBJECTIVES Describe the underlying Kubernetes architecture that supports OpenShift and how to access and identify key OpenShift cluster services by using both the web console and command-line utilities. Deploy the OpenShift Virtualization operator and examine the configuration options for the operator. Create, manage, and monitor virtual machines in Red Hat OpenShift Virtualization. Use comprehensive and flexible networking for virtual machines within an OpenShift environment. Configure and manage persistent storage for virtual machines, protect VM data through snapshots, export and import virtual machine images, and efficiently create virtual machine golden images by using cloning within a Red Hat OpenShift Virtualization environment. CONTENT Introduction to Kubernetes and OpenShift Describe the underlying Kubernetes architecture that supports OpenShift and how to access and identify key OpenShift cluster services by using both the web console and command-line utilities Introduction to Red Hat OpenShift Virtualization Deploy the OpenShift Virtualization operator and examine the configuration options for the operator. Creating, Managing, and Monitoring Virtual Machines in Red Hat OpenShift Virtualization Create, manage, and monitor virtual machines in Red Hat OpenShift Virtualization. Managing Virtual Machine Networking in Red Hat OpenShift Virtualization Use comprehensive and flexible networking for virtual machines within an OpenShift environment. Managing Storage for Virtual Machines in Red Hat OpenShift Virtualization Configure and manage persistent storage for virtual machines, protect VM data through snapshots, export and import virtual machine images, and efficiently create golden images for virtual machines by using cloning within a Red Hat OpenShift Virtualization environment

OVERVIEW Create and manage virtual machines by using the Red Hat OpenShift Virtualization operator. Red Hat OpenShift Virtualization Administration I: Operating Virtual Machines teaches the essential skills required to create and manage virtual machines (VMs) on OpenShift by using the Red Hat OpenShift Virtualization operator. OBJECTIVES Describe the underlying Kubernetes architecture that supports OpenShift and how to access and identify key OpenShift cluster services by using both the web console and command-line utilities. Deploy the OpenShift Virtualization operator and examine the configuration options for the operator. Create, manage, and monitor virtual machines in Red Hat OpenShift Virtualization. Use comprehensive and flexible networking for virtual machines within an OpenShift environment. Configure and manage persistent storage for virtual machines, protect VM data through snapshots, export and import virtual machine images, and efficiently create virtual machine golden images by using cloning within a Red Hat OpenShift Virtualization environment. CONTENT Introduction to Kubernetes and OpenShift Describe the underlying Kubernetes architecture that supports OpenShift and how to access and identify key OpenShift cluster services by using both the web console and command-line utilities Introduction to Red Hat OpenShift Virtualization Deploy the OpenShift Virtualization operator and examine the configuration options for the operator. Creating, Managing, and Monitoring Virtual Machines in Red Hat OpenShift Virtualization Create, manage, and monitor virtual machines in Red Hat OpenShift Virtualization. Managing Virtual Machine Networking in Red Hat OpenShift Virtualization Use comprehensive and flexible networking for virtual machines within an OpenShift environment. Managing Storage for Virtual Machines in Red Hat OpenShift Virtualization Configure and manage persistent storage for virtual machines, protect VM data through snapshots, export and import virtual machine images, and efficiently create golden images for virtual machines by using cloning within a Red Hat OpenShift Virtualization environment

OVERVIEW Learn to build and manage containers for deployment on a Kubernetes and Red Hat OpenShift cluster Introduction to Containers, Kubernetes, and Red Hat OpenShift (DO180) helps you build core knowledge in managing containers through hands-on experience with containers, Kubernetes, and the Red Hat® OpenShift® Container Platform. These skills are needed for multiple roles, including developers, administrators, and site reliability engineers. This course is based on Red Hat OpenShift Container Platform 4.2.  OBJECTIVES This course is for: Developers who wish to containerize software applications Administrators who are new to container technology and container orchestration Architects who are considering using container technologies in software architectures Site reliability engineers who are considering using Kubernetes and Red Hat OpenShift As a result of attending this course, you should be able to perform these basic tasks in Red Hat OpenShift Container Platform: Create containerized services using Podman. Manage containers and container images. Create custom container images. Deploy containerized applications on Red Hat OpenShift. Deploy multi-container applications. CONTENT Course introduction Introduce and review the course. Get started with container technology Describe how software can run in containers orchestrated by Red Hat OpenShift Container Platform. Create containerized services Provision a server using container technology. Manage containers Manipulate pre-build container images to create and manage containerized services. Manage container images Manage the life cycle of a container image from creation to deletion. Create custom container images Design and code a Docker file to build a custom container image. Deploy containerized applications on Red Hat OpenShift Deploy single container applications on Red Hat OpenShift Container Platform. Deploy multi-container applications Deploy applications that are containerized using multiple container images. Troubleshoot containerized applications Troubleshoot a containerized application deployed on Red Hat OpenShift. Comprehensive review of Introduction to Container, Kubernetes, and Red Hat OpenShift Demonstrate how to containerize a software application, test it with Docker, and deploy it on a Red Hat OpenShift cluster.   Note: Course outline is subject to change with technology advances and as the nature of the underlying job evolves. For questions or confirmation on a specific objective or topic, contact a training specialist.

OVERVIEW A developer introduction to building and managing containers with Podman for deploying applications on Red Hat OpenShift Container Platform. Red Hat OpenShift Development I: Introduction to Containers with Podman (DO188) introduces students to building, running, and managing containers with Podman and Red Hat OpenShift Container Platform. This course helps students build the core skills for developing containerized applications through hands-on experience. This course is based on Red Hat® Enterprise Linux® 8.6 and OpenShift® Container Platform 4.10. OBJECTIVES Introduction to containers Run containers with Podman Build custom container images Manage container images Remote debugging with containers Basic container networking Persist data with containers Run multi-container applications Troubleshoot Container Deployments Orchestrate containers with OpenShift and Kubernetes   CONTENT Introduction and Overview of Containers  Describe how containers facilitate application development.  Podman Basics  Manage and run containers with Podman.  Container Images  Navigate container registries to find and manage container images.  Container Networking  Describe basic container networking and how to access containerized services.  Persisting Data  Build persistent databases.  Custom Container Images  Build custom container images to containerize applications.  Multi-container Applications with Compose  Run multi-container applications using Compose.  Troubleshooting Containers  Analyze container logs and configure a remote debugger.  Container Orchestration with Kubernetes and OpenShift  Orchestrate containerized applications with Kubernetes and OpenShift.