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43.487 resultaten

Managing Enterprise Automation with Red Hat Ansible Automation Platform (AU467) - RHLS-Course [AU467LS]

OVERVIEW Manage complex Red Hat Ansible automation workflows at scale and prevent single points of failure. Managing Enterprise Automation with Red Hat Ansible Automation Platform (AU467) is for experienced DevOps engineers or Linux system administrators who want to centralize and control their automation execution at scale and privately share Ansible content across their organizations. This course is based on Red Hat Ansible Automation Platform 2.5. OBJECTIVES Discussion of the architecture of Red Hat Ansible Automation Platform 2. Installation and configuration of automation controllers and private automation hubs to centrally coordinate and scale Red Hat Ansible Automation Platform. Integration of Red Hat Ansible Automation Platform with centralized Git repository. Management of users, teams, and access permissions in Red Hat Ansible Automation Platform services. Creation and management of workflows that execute automation based on the success or failure of previous jobs. Configuration and management of automation mesh to distribute execution between automation controller and remote execution nodes. Troubleshooting and maintenance of Red Hat Ansible Automation Platform services. Discussion of recommended practices to ensure high availability and scalability of a large automation cluster. CONTENT Installing Red Hat Ansible Automation Platform Explain what Red Hat Ansible Automation Platform is and explore installation strategies. Managing user access Create user accounts, organize users into teams, and assign roles to administer and access Ansible Automation Platform resources. Managing inventories and machine credentials Create inventories of machines to manage, and configure credentials necessary for automation controller's execution nodes to log in and run Ansible jobs on those systems. Managing projects and launch Ansible jobs Create projects and job templates in the Ansible Automation Platform unified UI, and use them to launch Ansible Playbooks that are stored in Git repositories, in order to automate tasks on managed hosts. Configuring advanced job configuration Configure advanced job template features to more effectively and efficiently implement jobs. Constructing job workflows Assemble existing job templates into a sequential, branching workflow that can launch multiple jobs, run jobs to recover from a preceding failure in the workflow, and request user approval before the workflow can be advanced past a certain point. Managing advanced inventories Manage inventories that are dynamically generated from external data sources by using plug-ins, or are constructed from a filtered set of hosts in existing inventories. Automating configuration of Ansible Automation Platform Automate the configuration and deployment of Red Hat Ansible Automation Platform services by using Ansible Content Collections, the automation controller API, and Git webhooks. Maintaining Red Hat Ansible Automation Platform Perform routine maintenance and administration of Red Hat Ansible Automation Platform. Building a large scale Red Hat Ansible Automation Platform deployment Scale up your Red Hat Ansible Automation Platform deployment by using automation mesh and dispersed execution nodes. Comprehensive review Demonstrate skills learned in this course by configuring private automation hub and by configuring and operating a new organization in automation controller using a provided specification, Ansible projects, and hosts to be provisioned and managed.
€3.740
E-Learning

Designing, Deploying and Managing Network Automation Systems [AUTOCOR]

VIRTUAL TRAINING CENTER ma 10 aug. 2026 en 9 andere data
OVERVIEW Be prepared for a professional role as a network automation engineer. The Designing, Deploying and Managing Network Automation Systems (AUTOCOR) course focuses on designing and implementing automation systems, from writing Python scripts and Ansible playbooks, and applying Terraform for network automation, to building complex CI/CD pipelines with multiple tools. The Designing, Deploying and Managing Network Automation Systems (AUTOCOR) course prepares you for a professional role as a network automation engineer. It focuses on designing and implementing automation systems, from writing Python scripts and Ansible playbooks, and applying Terraform for network automation, to building complex CI/CD pipelines that integrate multiple tools. The training also shows how to leverage AI for network automation by building Large Language Model (LLM)-powered network agents and by using MCP servers. Additionally, the training focuses on operational aspects of managing a modern, automated network and explores secure coding practices, collecting logs, containerization, and model-driven telemetry. Overall, the training focuses on practical implementation that directly prepares you to design, deploy, and operate automated networks. This training also prepares you for the 350-901 AUTOCOR exam. If passed, you earn the Cisco Certified Specialist - Automation Core certification and satisfy the core exam requirement for the Cisco Certified Network Professional (CCNP) Automation and Cisco Certified Internetwork Expert (CCIE) Automation certifications. This training also earns you 32 Continuing Education (CE) credits toward recertification. OBJECTIVES Key outcomes: Evaluate various network automation tools and approaches Use Python for CLI-based network automation Integrate REST APIs in network automation workflows Automate device configuration using RESTCONF requests based on YANG data models Create network automation solutions with Ansible Create network automation solutions with Terraform Implement the Infrastructure as Code approach for network management Use Git to track network changes Design and build GitLab CI pipelines for network automation Integrate CML topologies in automated workflows Create network validation tools with pyATS and include them in automated workflows Configure model-driven telemetry streams to collect real-time operational data from Cisco devices Diagnose common automation failures using well-structured logs from Python, Ansible, and RESTCONF integrations Harden network automation code by validating inputs, protecting credentials, and sanitizing outputs Build and run multi-service Docker Compose environments for network automation Generate, sign, and install certificates to secure web interfaces and APIs used by network automation tools Describe the role, value, and risks of generative AI in network automation script creation Create AI agents for network automation Integrate LLMs with external capabilities using MCP servers AUDIENCE - Individuals seeking the CCNP Automation certification - Network Automation Engineers - Network Engineers with coding experience - DevOps Engineers working with network infrastructure - System Engineers - Network Site Reliability Engineers (SREs) CERTIFICATION The CCNP Automation certification confirms a candidate's knowledge of network automation systems development and design including infrastructure as code, operations, and AI in automation. Technologies included are Cisco IOS XE, Cisco ACI, Cisco Meraki, Cisco Catalyst Center, Cisco SD-WAN, Cisco Identity Services Engine and Webex Messaging. Certification Title: CCNP Automation NEXT STEP ENAUTO - Automating and Programming Cisco Enterprise Solutions DCNAUTO - Automating Cisco Data Center Networking Solutions CONTENT Outline: Network Automation Toolkits Network Task Automation with Python REST APIs in Network Automation Network Automation with Ansible Network Automation with Terraform Infrastructure as Code Implementation Network Change Tracking with Git Configuration Change Deployment with CI Pipelines Cisco Modeling Labs Integration for Test Network Environments Network State Validation with pyATS Model-Driven Telemetry for Network Monitoring Network Automation Solution Troubleshooting Secure Coding Practices for Network Automation Network Automation Environment Containerization with Docker Compose Trusted TLS Certificates Deployment for Secure Communication Generative AI for Network Automation AI Agents for Network Automation LLM and MCP Server Integration LABS: Use Python to Automate Common Network Tasks Explore REST API Documentation Automate API Calls with Python Requests Construct and Send RESTCONF Requests Automate the Device Configuration with RESTCONF Create a Network Automation Solution with Ansible Automate Network Infrastructure with Terraform Manage Router Interfaces as Code Start Tracking Your Network State with GitLab Build a GitLab CI Pipeline for Network Configuration Create a Testing Network Environment with Cisco Modeling Labs Build a Python Script to Launch Test Topologies in Cisco Modeling Labs Integrate Cisco Modeling Labs Topologies into CI Pipeline Create a Configuration Validation Tool with pyATS Integrate pyATS Testing into Automated Pipelines Set Up MDT on a Cisco Router Using YANG Suite Troubleshoot an Automation Script Harden an Automation Script Containerize Automation Components Set Up Local LLM with Ollama Build a Network Automation Tool with Python and Ollama Build and Launch a FastMCP Server
€4.095
Klassikaal
max 16

Designing, Deploying and Managing Network Automation Systems [AUTOCOR-CPLL]

OVERVIEW Be prepared for a professional role as a network automation engineer. The Designing, Deploying and Managing Network Automation Systems (AUTOCOR) course focuses on designing and implementing automation systems, from writing Python scripts and Ansible playbooks, and applying Terraform for network automation, to building complex CI/CD pipelines with multiple tools. The Designing, Deploying and Managing Network Automation Systems (AUTOCOR) course prepares you for a professional role as a network automation engineer. It focuses on designing and implementing automation systems, from writing Python scripts and Ansible playbooks, and applying Terraform for network automation, to building complex CI/CD pipelines that integrate multiple tools. The training also shows how to leverage AI for network automation by building Large Language Model (LLM)-powered network agents and by using MCP servers. Additionally, the training focuses on operational aspects of managing a modern, automated network and explores secure coding practices, collecting logs, containerization, and model-driven telemetry. Overall, the training focuses on practical implementation that directly prepares you to design, deploy, and operate automated networks. This training also prepares you for the 350-901 AUTOCOR exam. If passed, you earn the Cisco Certified Specialist - Automation Core certification and satisfy the core exam requirement for the Cisco Certified Network Professional (CCNP) Automation and Cisco Certified Internetwork Expert (CCIE) Automation certifications. This course is approx 32 hours in duration and will earn you 32 Continuing Education (CE) credits toward recertification. OBJECTIVES Key outcomes: Evaluate various network automation tools and approaches Use Python for CLI-based network automation Integrate REST APIs in network automation workflows Automate device configuration using RESTCONF requests based on YANG data models Create network automation solutions with Ansible Create network automation solutions with Terraform Implement the Infrastructure as Code approach for network management Use Git to track network changes Design and build GitLab CI pipelines for network automation Integrate CML topologies in automated workflows Create network validation tools with pyATS and include them in automated workflows Configure model-driven telemetry streams to collect real-time operational data from Cisco devices Diagnose common automation failures using well-structured logs from Python, Ansible, and RESTCONF integrations Harden network automation code by validating inputs, protecting credentials, and sanitizing outputs Build and run multi-service Docker Compose environments for network automation Generate, sign, and install certificates to secure web interfaces and APIs used by network automation tools Describe the role, value, and risks of generative AI in network automation script creation Create AI agents for network automation Integrate LLMs with external capabilities using MCP servers CONTENT Outline: Network Automation Toolkits Network Task Automation with Python REST APIs in Network Automation Network Automation with Ansible Network Automation with Terraform Infrastructure as Code Implementation Network Change Tracking with Git Configuration Change Deployment with CI Pipelines Cisco Modeling Labs Integration for Test Network Environments Network State Validation with pyATS Model-Driven Telemetry for Network Monitoring Network Automation Solution Troubleshooting Secure Coding Practices for Network Automation Network Automation Environment Containerization with Docker Compose Trusted TLS Certificates Deployment for Secure Communication Generative AI for Network Automation AI Agents for Network Automation LLM and MCP Server Integration LABS: Use Python to Automate Common Network Tasks Explore REST API Documentation Automate API Calls with Python Requests Construct and Send RESTCONF Requests Automate the Device Configuration with RESTCONF Create a Network Automation Solution with Ansible Automate Network Infrastructure with Terraform Manage Router Interfaces as Code Start Tracking Your Network State with GitLab Build a GitLab CI Pipeline for Network Configuration Create a Testing Network Environment with Cisco Modeling Labs Build a Python Script to Launch Test Topologies in Cisco Modeling Labs Integrate Cisco Modeling Labs Topologies into CI Pipeline Create a Configuration Validation Tool with pyATS Integrate pyATS Testing into Automated Pipelines Set Up MDT on a Cisco Router Using YANG Suite Troubleshoot an Automation Script Harden an Automation Script Containerize Automation Components Set Up Local LLM with Ollama Build a Network Automation Tool with Python and Ollama Build and Launch a FastMCP Server
€825
E-Learning

Cisco Splunk for AI Operations [CAIOP-CPLL]

OVERVIEW The Cisco Splunk for AI Operations (CAIOP) Learning Path explores the foundational concepts, business drivers, and architectural vision of Artificial Intelligence for IT Operations (AIOps) and demonstrates how AIOps' core principles transform reactive IT into a proactive, intelligent, and strategic function. These principles, powered by machine learning and advanced data analytics and supported by Splunk's robust data platform and AI capabilities, drive business value, enhanced security, and resilience in complex, modern environments. This course is just under 8 hours in duration and is worth 6 Continuing Education Credits. OBJECTIVES After completing this course you should be able to: Understand AIOps fundamentals, business drivers, and architectural vision Identify how AIOps, AI, and machine learning address challenges in modern IT operations Use Splunk to ingest, process, and visualize machine data for actionable insights Apply machine learning techniques for event correlation, anomaly detection, and automation Design, implement, and scale AIOps solutions to drive efficiency, security, and business value CONTENT Cisco Splunk for AI Operations AIOps Fundamentals Splunk Essentials Data Engineering for AIOps Machine Learning with AIOps Building Machine Learning Models Operationalizing AIOps Scaling and Integrating AIOps AIOps Adoption and Future Trends
€265
E-Learning

Cisco Catalyst Center Foundations [CCFND-CPLL]

OVERVIEW The Cisco Catalyst Center Foundations (CCFND) training is designed to expand your knowledge of Cisco Catalyst Center, including its basics, deployment and scalability options, initial configurations, best practices, and integration with Cisco Identity Services Engine (ISE). The training will focus on network automation, network assurance, network security, and network programmability using Cisco Catalyst Center. Though this training is not related to a specific exam, it is highly encouraged to take this training as an introduction to topics found in the Implementing and Operating Cisco Enterprise Network Core Technologies (350-401 ENCOR) exam. This course is worth 44 Continuing Education (CE) Credits towards recertification OBJECTIVES After completing this course you should be able to: Learn about Cisco Catalyst Center product, intent-based networking, system architecture, and key features and use cases of Cisco Catalyst Center Use Cisco Catalyst Center automation (NetOps and SecOps), assurance (AIOps), and platform integration with DevOps in your enterprise network Deploy Cisco Catalyst Center based on pre-deployment requirements, and perform first-time setup procedures Describe high availability and scalability options for Cisco Catalyst Center, including clustering, link redundancy, and disaster recovery Explain Cisco Catalyst Center system settings, basic and advanced automation, device provisioning, and compliance audit procedures Configure Cisco Catalyst Center to onboard devices, integrate with ISE, automate device configurations, troubleshoot health of network devices, and track clients Describe Cisco SD-Access architecture, including fabric networking, underlay and overlay routing, and fabric node roles, and implement policy-based segmentation with group-based, IP-based, and application policies CONTENT Cisco Catalyst Center Overview Intent-Based Networking System Architecture Key Features and Use Cases Automation and NetOps Automation and SecOps Assurance and AIOps Platform and DevOps Licensing and Device Support Cisco Catalyst Center Deployment Deployment Options Pre-Deployment Requirements Maglev Installation First-Time Setup Application Installation Cisco Catalyst Center High Availability and Scalability Scalability High Availabiliy Options Three Node Cluster Considerations Link Redundancy Clustering Disaster Recovery Cisco Catalyst Center System Settings and Operations Basic Configuration System and Application Update Backup and Restore Cisco ISE Integration User Management Cisco Catalyst Center Inventory, Discovery, and Device Manageability Network Hierarchy Device Inventory Network Discovery Device Controllability Cisco Catalyst Center Basic Automation and Provisioning Basic Network Settings Automation Device Provisioning Configuration Monitoring and Compliance Audit Software Image Management Cisco Catalyst Center Advanced Automation and Day-0 Onboarding Network Profiles Wireless Design Model Configuration Configuration Templates Device Onboarding Using Plug-and-Play Cisco Prime Infrastructure to Cisco Catalyst Center Migration Cisco Prime Infrastructure to Cisco Catalyst Center Migration Options Migration Process and Tools Migration Assessment and Reporting Using Cisco PDART Data Migration Using Prime Data Migration Tool Cisco Prime Infrastructure and Cisco Catalyst Center Coexistence Cisco Catalyst Center Health and Performance Monitoring Cisco Catalyst Center Assurance Functional Components Cisco Catalyst Center Assurance Data Collection Cisco Catalyst Center Assurance Data Analytics and Metrics Cisco Catalyst Center Assurance Health Scores Cisco Catalyst Center Assurance Dashbard Time Ranges Concepts Cisco Catalyst Center Assurance Device, Client, and Application Health Network Device Health Device 360 View Client Health and Client 360 View Application Health and Application 360 View Reports in Cisco Catalyst Center Cisco Catalyst Center Issues, Insights, and Trends Monitoring Cisco AI Analytics Cisco AI Network Analytics - Anomaly Detection and Resolution Issues Operations AI-Driven Issues Resolution Trends, Insights, and Comparative Analysis Capacity, Security and Wi-Fi 6 Readiness Insights Cisco Catalyst Center Wireless Networks Monitoring and Troubleshooting Assurance Tools for Troubleshooting Wireless Networks Assurance-Correlated Insights for Wireless Networks Intelligent Capture for Troubleshooting Wireless Networks Intelligent Capture to Troubleshoot Clients Intelligent Capture to Collect AP-Based Statistics Cisco SD-Access Overview Cisco SD-Access Introduction Cisco SD-Access Fabric Networking Cisco SD-Access Fabric Nodes Wireless SD-Access Overview Cisco Catalyst Center Policies Cisco Catalyst Center Policies Group-Based Access Control Policies IP-Based Policies Application Policies Cisco Catalyst Center Endpoint Visibility Endpoint Visibilty Overview Group-Based Policy Analytics AI Endpoint Analytics Cisco Catalyst Center Platform Overview Cisco Catalyst Center Platfrom Overview Intent-Based APIs Cisco Catalyst Center SDKs and other Tools Cisco Catalyst Center Notification and Events Cisco Catalyst Center Event Webhooks Cisco Catalyst Center and Third-Party Integrations Cisco Catalyst Center Network and Assurance Automation Cisco Catalyst Center Network Automation Flow Cisco Catalyst Center Network Assurance Workflow Labs Discovery Lab 1: Integrate Cisco ISE and Cisco Catalyst Center Discovery Lab 2: Add Network Devices to Cisco Catalyst Center Discovery Lab 3: Automate Basic Device Configuration Using Cisco Catalyst Center Discovery Lab 4: Upgrade Network Device using Cisco Catalyst Center Discovery Lab 5: Automate Network Device Configuration Using a Template Discovery Lab 6: Onboard a Device Using PnP Discovery Lab 7: Troubleshoot the Health of Network Devices Discovery Lab 8: Monitor the Health of Clients and Applications Discovery Lab 9: Observe Assurance AI Network Analytics Discovery Lab 10: Monitor Wireless Networks with Advanced Assurance Tools Discovery Lab 11: Explore Cisco SD-Access Networks Discovery Lab 12: Deploy SD-Access Group-Based Access Control Policy Discovery Lab 13: Examine Cisco Catalyst Center APIs Discovery Lab 14: Examine Cisco Catalyst Center Webhooks Discovery Lab 15: Deploy IaaC Using Cisco Catalyst Center and Terraform Discovery Lab 16: Automate Cisco Catalyst Center Assurance Using Ansible
€790
E-Learning

Automating Networks Using Cisco Platforms [CCNAAUTO]

VIRTUAL TRAINING CENTER ma 27 jul. 2026 en 9 andere data
OVERVIEW Discover how to implement network applications and automation workflows across network, security, collaboration, and computing infrastructure. The Automating Networks Using Cisco Platforms (CCNAAUTO) training teaches you how to implement basic network applications using Cisco platforms as a base, and how to implement automation workflows across network, security, collaboration, and computing infrastructure. The training gives you hands-on experience solving real-world problems using Cisco Application Programming Interfaces (APIs) and modern development tools. This course also prepares you for the 200-901 CCNAAUTO v1.1 exam. If passed, you earn the Cisco Certified Network Associate (CCNA) Automation certification. This training also earns you 48 Continuing Education (CE) credits toward recertification. OBJECTIVES After taking this course, you should be able to: Describe the importance of APIs and use of version control tools in modern software development Describe common processes and practices used in software development Describe options for organizing and constructing modular software Describe HTTP concepts and how they apply to network-based APIs Apply Representational State Transfer (REST) concepts to integration with HTTP-based APIs Describe Cisco platforms and their capabilities Describe programmability features of different Cisco platforms Describe basic networking concepts and interpret simple network topology Describe interaction of applications with the network and tools used for troubleshooting issues Apply concepts of model-driven programmability to automate common tasks with Python scripts Identify common application deployment models and components in the development pipeline Utilize tools to automate infrastructure through scripting and model-driven programmability Describe common security concerns and types of tests, and utilize containerization for local development AUDIENCE - Network Automation Engineers - Software Developers - System Integration Programmers - Infrastructure Architects - Network Designers CERTIFICATION The CCNA Automation certification validates your knowledge of software development and design, including APIs, Cisco platforms, applications, security, automation, and more. Certification Title: CCNA Automation NEXT STEP AUTOCOR - Designing, Deploying and Managing Network Automation Systems CONTENT Course Outline: Practicing Modern Software Development Describing Software Development Process Designing Software Introducing Network-Based APIs Consuming REST-Based APIs Introducing Cisco Platforms and APIs Employing Programmability on Cisco Platforms Describing IP Networks Relating Network and Applications Employing Model-Driven Programmability Deploying Applications Automating Infrastructure Testing and Securing Applications Lab Code Reference LABS: Parse API Data Formats with Python Use Git for Version Control Identify Software Architecture and Design Patterns on a Diagram Implement Singleton Pattern and Abstraction-Based Method Inspect HTTP Messages Use Postman Troubleshoot an HTTP Error Response Utilize APIs with Python Use the Cisco Webex Collaboration API Interpret a Basic Network Topology Diagram Identify the Cause of Application Connectivity Issues Perform Basic NETCONF Operations Utilize Bash Commands for Local Development Construct Infrastructure Automation Workflow Construct a Python Unit Test Interpret a Dockerfile Utilize Docker Commands to Manage Local Developer Environment Exploit Insufficient Parameter Sanitization
€4.095
Klassikaal
max 16

Automating Networks Using Cisco Platforms (CCNAAUTO) [CCNAAUTO-CPLL]

OVERVIEW Discover how to implement network applications and automation workflows across network, security, collaboration, and computing infrastructure. The Automating Networks Using Cisco Platforms (CCNAAUTO) course teaches you how to implement basic network applications using Cisco platforms as a base, and how to implement automation workflows across network, security, collaboration, and computing infrastructure. The training gives you hands-on experience solving real-world problems using Cisco Application Programming Interfaces (APIs) and modern development tools. This course also prepares you for the 200-901 CCNAAUTO v1.1 exam. If passed, you earn the Cisco Certified Network Associate (CCNA) Automation certification. This training also earns you 48 Continuing Education (CE) credits toward recertification. OBJECTIVES After taking this course, you should be able to: Describe the importance of APIs and use of version control tools in modern software development Describe common processes and practices used in software development Describe options for organizing and constructing modular software Describe HTTP concepts and how they apply to network-based APIs Apply Representational State Transfer (REST) concepts to integration with HTTP-based APIs Describe Cisco platforms and their capabilities Describe programmability features of different Cisco platforms Describe basic networking concepts and interpret simple network topology Describe interaction of applications with the network and tools used for troubleshooting issues Apply concepts of model-driven programmability to automate common tasks with Python scripts Identify common application deployment models and components in the development pipeline Utilize tools to automate infrastructure through scripting and model-driven programmability Describe common security concerns and types of tests, and utilize containerization for local development CONTENT Course Outline: Practicing Modern Software Development Describing Software Development Process Designing Software Introducing Network-Based APIs Consuming REST-Based APIs Introducing Cisco Platforms and APIs Employing Programmability on Cisco Platforms Describing IP Networks Relating Network and Applications Employing Model-Driven Programmability Deploying Applications Automating Infrastructure Testing and Securing Applications Lab Code Reference Labs: Parse API Data Formats with Python Use Git for Version Control Identify Software Architecture and Design Patterns on a Diagram Implement Singleton Pattern and Abstraction-Based Method Inspect HTTP Messages Use Postman Troubleshoot an HTTP Error Response Utilize APIs with Python Use the Cisco Webex Collaboration API Interpret a Basic Network Topology Diagram Identify the Cause of Application Connectivity Issues Perform Basic NETCONF Operations Utilize Bash Commands for Local Development Construct Infrastructure Automation Workflow Construct a Python Unit Test Interpret a Dockerfile Utilize Docker Commands to Manage Local Developer Environment Exploit Insufficient Parameter Sanitization
€825
E-Learning

Red Hat OpenStack Administration I: Core Operations for Domain Operators (CL110) - RHLS-Course [CL110LS]

OVERVIEW Learn how to install configure, use, and maintain Red Hat OpenStack Platform Red Hat OpenStack Administration I (CL110) is designed for system administrators who are intending to implement a cloud computing environment using Red Hat OpenStack® Platform. This course is based on Red Hat Enterprise Linux OpenStack Platform 8. This course will teach students to install a proof-of-concept, configure, use, and maintain Red Hat OpenStack Platform. This course covers the core services: identity (Keystone), block storage (Cinder), image (Glance), networking (Neutron), compute and controller (Nova), and dashboard (Horizon). This course is designed for Linux system administrators, cloud administrators, and cloud operators interested in, or responsible for, maintaining a private or hybrid cloud. OBJECTIVES As a result of attending this course, you will understand the architecture of a private or hybrid OpenStack cloud infrastructure and will be able to create, manage, and troubleshoot software-defined network services, resources, servers, and applications for dynamically scalable business environments. You should also be able to demonstrate these skills: Design and implement on-demand projects, software-defined networks, and virtual machine instances. Deploy a proof-of-concept OpenStack installation for practice, development, demonstration, and testing, back in your own home or business computing environment. Manage software-defined networks such as subnets, routers, floating IP addresses, images, flavors, security groups/rules, and block and object storage. Create and customize advanced VM instances as applications, customize on deploy, and create scalable stacks of multiple VM applications. CONTENT Course introduction Introduce and review the course.                Launch an instance Launch an instance and describe the terminology and services used in OpenStack. Manage projects and users Manage projects and users using Horizon. Manage project quotas Manage project quotas using Horizon. Manage flavors Manage flavors using Horizon. Manage images Manage images using Horizon. Manage networks Manage networks using Horizon. Manage floating IP addresses Manage networks using Horizon. Manage block storage Manage block storage using Horizon. Manage security and access Manage security and access to instances using Horizon. Manage instances with Horizon Manage instances using Horizon. Install OpenStack Install an OpenStack proof of concept using PackStack. Manage the Keystone identity service Manage the Keystone identity service using the command-line interface. Prepare to launch instances with the command-line interface Prepare to launch instances and manage instances using the command-line interface. Manage instances with the command-line interface Manage instances using the command-line interface. Manage block storage with the command-line interface Manage block storage using the command-line interface. Comprehensive review of Red Hat OpenStack Administration I Review tasks in the Red Hat OpenStack Administration I course.  
€3.200
E-Learning

OpenStack Administration: Control Plane Management (CL170) - RHLS-Course [CL170LS]

OVERVIEW Use Red Hat OpenStack Services on OpenShift and RHEL compute nodes that run VM-based workloads. OpenStack Administration: Control Plane Management (CL170) helps Red Hat OpenStack cluster administrators to manage the health and performance of OpenStack control plane services, to troubleshoot issues by inspecting Kubernetes operators and workloads, and to configure OpenStack control plane services by using Kubernetes custom resources. This course is based on Red Hat OpenShift Services on OpenStack 18. OBJECTIVES Check the health of OpenStack operators and workloads and identify disabled or misconfigured services Collect troubleshooting information from OpenStack control and data planes for customer support requests Enable and customize OpenStack control plane services by configuring the control plane custom resource Check the health of OpenStack compute notes and identify missing or misconfigured data plane services Remove and replace or reprovision failed compute nodes CONTENT Introduction to Red Hat OpenShift Container Platform Identify the Red Hat OpenShift architecture and resources, navigate the graphical and command-line interfaces, and find information about commands. Inspecting OpenStack Services on OpenShift Identify OpenStack services on OpenShift and assess the health of the OpenStack operator and its dependent resources. Customizing OpenStack Services Enable and disable OpenStack services and customize them. Verifying OpenStack API Connectivity Identify the resources that connect an OpenStack control plane to its data plane. Verifying Connectivity to OpenStack Cell Services Verify that an OpenStack cell is connected to its database and messaging services, and validate the additional services that enable connections to it from compute nodes. Accessing Storage Resources in OpenStack Verify the status and connectivity of OpenStack storage resources. Verifying Reliable OpenStack Services Configure and assess high availability of an OpenStack control plane and its supporting services. Verifying Network Encryption for OpenStack Services Inspect the configuration of OpenStack components and verify that the network communication uses certificate-based encryption. Inspecting Data Plane Services and Compute Nodes Identify OpenStack data plane resources and assess their health. Customizing an OpenStack Data Plane Apply custom configuration to data plane node sets and verify the applied settings.
€1.870
E-Learning

OpenStack Administration: Control Plane Management (CL170) - RHLS-Course [CL170LS]

OVERVIEW Use Red Hat OpenStack Services on OpenShift and RHEL compute nodes that run VM-based workloads. OpenStack Administration: Control Plane Management (CL170) helps Red Hat OpenStack cluster administrators to manage the health and performance of OpenStack control plane services, to troubleshoot issues by inspecting Kubernetes operators and workloads, and to configure OpenStack control plane services by using Kubernetes custom resources. This course is based on Red Hat OpenShift Services on OpenStack 18. OBJECTIVES Check the health of OpenStack operators and workloads and identify disabled or misconfigured services Collect troubleshooting information from OpenStack control and data planes for customer support requests Enable and customize OpenStack control plane services by configuring the control plane custom resource Check the health of OpenStack compute notes and identify missing or misconfigured data plane services Remove and replace or reprovision failed compute nodes CONTENT Introduction to Red Hat OpenShift Container Platform Identify the Red Hat OpenShift architecture and resources, navigate the graphical and command-line interfaces, and find information about commands. Inspecting OpenStack Services on OpenShift Identify OpenStack services on OpenShift and assess the health of the OpenStack operator and its dependent resources. Customizing OpenStack Services Enable and disable OpenStack services and customize them. Verifying OpenStack API Connectivity Identify the resources that connect an OpenStack control plane to its data plane. Verifying Connectivity to OpenStack Cell Services Verify that an OpenStack cell is connected to its database and messaging services, and validate the additional services that enable connections to it from compute nodes. Accessing Storage Resources in OpenStack Verify the status and connectivity of OpenStack storage resources. Verifying Reliable OpenStack Services Configure and assess high availability of an OpenStack control plane and its supporting services. Verifying Network Encryption for OpenStack Services Inspect the configuration of OpenStack components and verify that the network communication uses certificate-based encryption. Inspecting Data Plane Services and Compute Nodes Identify OpenStack data plane resources and assess their health. Customizing an OpenStack Data Plane Apply custom configuration to data plane node sets and verify the applied settings.
€1.870
E-Learning