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Duration 5 Days 30 CPD hours This course is intended for Experienced system administrators, system integrators, and consultants responsible for implementing and managing VMware Cloud Foundation Overview By the end of the course, you should be able to meet the following objectives: Describe design implications of VMware Cloud Foundation standard or consolidated architecture List requirements for VMware Cloud Foundation deployment Describe the VMware Cloud Foundation bring-up process and the VMware Cloud Foundation architecture Perform VMware Cloud Foundation bring-up Describe physical and virtual networking considerations Outline VMware Cloud Foundation storage options Use the VMware Imaging Appliance to image ESXi hosts Describe VMware Cloud Foundation multi-instance federation Use VMware Cloud Foundation™ SDDC Manager™ to perform operational tasks Describe user roles in VMware Cloud Foundation and VMware vSphere Manage users and passwords using VMware Cloud Foundation Manage certificate rotation for VMware Cloud Foundation components Use Active Directory integration to automate certificate generation and rotation Describe workload domains Manage workload domains in VMware Cloud Foundation Manage VMware NSX-T™ for VMware Cloud Foundation Describe use cases for Application Virtual Networks (AVNs) Meet vSphere with VMware Tanzu™ requirements Deploy a vSphere with Tanzu enabled workload domain Manage VMware vSAN™ storage in a workload domain Create vSAN storage policies Describe Cloud Native storage Describe the importance of business continuity measures in VMware Cloud Foundation Plan appropriate backup and restore workflows for VMware Cloud Foundation components Implement stretched clusters in VMware Cloud Foundation workload domains This five-day course includes instruction on the capabilities of VMware Cloud Foundation? and how to successfully plan, deploy, manage, and operate hybrid and cloud infrastructures, including customization. The course explains the architecture of VMware Cloud Foundation and explains licensing, certificates, and storage and network management. The course also covers workload domains, availability, life cycle management, and troubleshooting. Course Introduction Introductions and course logistics Course objectives VMware Cloud Foundation Overview Describe the VMware Cloud Foundation solution Describe VMware Cloud Foundation architecture Identify VMware Cloud Foundation components Describe VMware Cloud Foundation topology Define VMware Cloud Foundation terminology Day Zero Tasks Identify the requirements for deploying VMware Cloud Foundation Identify management domain sizing considerations Identify workload domain sizing considerations Detail design considerations for ESXi in management and VI workload domains Detail design considerations for vCenter in management and VI workload domains Detail the VMware Cloud Foundation bring-up process Identify information required for the Planning and Preparation Workbook Identify information required for the Deployment Parameter Workbook Describe how VMware Cloud Builder automates the deployment process Explain how the Deployment Parameter Workbook is imported into VMware Cloud Builder Recognize the configuration validation process performed by VMware Cloud Builder Detail the deployment of the management domain Recognize the options to image a host Identify the key capabilities of VIA Recognize how to use VIA for imaging the ESXi Nodes Post Deployment Operations Understand VMware Cloud Foundation integration with VMware Single Sign-On Configure user access to VMware Cloud Foundation Describe the importance of user roles in vSphere Configure identity sources in vSphere to use with VMware Cloud Foundation Manage passwords in VMware Cloud Foundation Explain the importance of using VMware Cloud Foundation to manage passwords for vSphere components Detail the best practices for password management for VMware Cloud Foundation Retrieve and secure the password list Describe the process for rotating passwords not managed by VMware Cloud Foundation VMware Cloud Foundation License Management Describe how to add license keys to the VMware Cloud Foundation inventory Describe how to view license keys in SDDC Manager Describe how to assign license keys Describe how to remove license keys Describe how to replace expiring licenses VMware Cloud Foundation Networking with NSX-T Describe NSX Management plane and Control planes Detail design considerations for workload domains with shared NSX Manager instances Detail design considerations for workload domains with dedicated NSX Manager instances Describe the spine-and-leaf design Describe the addressing scheme for the underlay Recognize possible variations of the spine-and-leaf design Describe the multi-NIC design Describe NSX Edge node design and BGP peering with the physical network Describe cluster design and rack design Explain dynamic routing with BGP Explain virtual IP addressing Describe logical switching Detail NSX Edge functions Define application virtual networks Describe management domain rack options List NSX Edge cluster requirements for vSphere with Tanzu Discuss NSX Edge cluster placement considerations Describe NSX-T Data Center deployment in VMware Cloud Foundation Explain how logical routing works in NSX-T Data Center Identify NSX Edge functions Describe data plane preparation for NSX-T Data Center Edge nodes in a workload domain Recognize Tier-0 and Tier-1 gateway topologies Recognize features of NSX distributed firewalls Describe the benefits of NSX Federation in VMware Cloud Foundation Identify NSX Federation Use Cases Explain NSX Federation Components and Architecture Discuss NSX Federation configuration basics Managing Workload Domains Define workload domains Detail design considerations for vSphere networking in management and VI workload domains Detail design considerations for storage in management and VI workload domains Recognize design choices for a consolidated design or standard design List the types of workload domains State scale limits for workload domains Identify use cases for multiple clusters in a workload domain List workload domain prerequisites Explain how to create a workload domain Describe how to scale a workload domain Explain how to delete a workload domain Describe host decommissioning vSphere with Tanzu in VMware Could Foundation Discuss the vSphere with Tanzu solution Define the role of Spherelet Describe the supervisor cluster control plane Define vSphere with Tanzu namespaces Describe the role of NSX-T networking components Discuss vSphere with Tanzu networking topology Describe VMware Container Networking with Antrea Describe control plane VM management networking requirements Plan appropriate IP address CIDR ranges for pod, ingress, and egress networking Describe prerequisites for vSphere with Tanzu cluster compatibility Deploy vSphere with Kubernetes Create a vSphere namespace Configure limits and permissions for a vSphere namespace Enabling Harbor Image Registry VMware Cloud Foundation Storage Management Identify vSAN architecture and components Recognize storage options for VMware Cloud Foundation Recognize the connectivity options for supplemental storage Explain why vSAN is the best option for VMware Cloud Foundation storage Recognize vSAN design considerations Identify sizing and performance considerations that impact the storage design Describe vSAN requirements for the management and workload domains Define deduplication and compression Discuss how to scale vSAN clusters in VMware Cloud Foundation Explain how storage policies work with VMware Cloud Foundation vSAN clusters Explain storage policy failure tolerance rules Identify a VM storage policy compliance status Relate storage policies to Kubernetes storage classes Describe persistent volumes Monitor Cloud Native Storage in the vSphere Client Availability and Business Continuity Identify steps in the SDDC Manager backup and restore process Recognize the importance of external service availability Describe native vSphere availability options Identify steps in the NSX backup and restore process Identify stretched cluster use cases Identify stretched cluster components Recognize stretched cluster requirements in VMware Cloud Foundation Prepare and deploy a vSAN stretched cluster using APIs VMware Cloud Foundation Certificate Management Describe public key infrastructure (PKI) Explain the purpose of certificate signing requests (CSRs) List the available CA options in SDDC Manager Describe how to view certificates Explain how to generate a CSR Describe how to replace and install certificates for VMware Cloud Foundation components List the available CA options in SDDC Manager Explain how to configure the Microsoft CA server Describe how to install certificates issued by the Microsoft CA server Explain how to add OpenSSL CA in SDDC Manager Describe how to install certificates issued by OpenSSL CA Explain how to install certificates issued by third-party CAs

Duration 1 Days 6 CPD hours This course is intended for Individuals planning to deploy applications and create application environments on Google Cloud. Developers, systems operations professionals, and solution architects getting started with Google Cloud. Executives and business decision makers evaluating the potential of Google Cloud to address their business needs. Overview Identify the purpose and value of Google Cloud products and services. Interact with Google Cloud services. Describe ways in which customers have used Google Cloud. Choose among and use application deployment environments on Google Cloud: App Engine, Google Kubernetes Engine, and Compute Engine. Choose among and use Google Cloud storage options: Cloud Storage, Cloud SQL, Cloud Bigtable, and Firestore. Make basic use of BigQuery, Google's managed data warehouse for analytics. This course uses lectures, demos, and hands-on labs to give you an overview of Google Cloud products and services so that you can learn the value of Google Cloud and how to incorporate cloud-based solutions into your business strategies. Introducing Google Cloud Platform Explain the advantages of Google Cloud Platform. Define the components of Google's network infrastructure, including: Points of presence, data centers, regions, and zones. Understand the difference between Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS). Getting Started with Google Cloud Platform Identify the purpose of projects on Google Cloud Platform. Understand the purpose of and use cases for Identity and Access Management. List the methods of interacting with Google Cloud Platform. Lab: Getting Started with Google Cloud Platform. Google Compute Engine and Networking Identify the purpose of and use cases for Google Compute Engine. Understand the basics of networking in Google Cloud Platform. Lab: Deploying Applications Using Google Compute Engine. Google Cloud Platform Storage Options Understand the purpose of and use cases for: Google Cloud Storage, Google Cloud SQL, and Google Cloud Bigtable. Learn how to choose between the various storage options on Google Cloud Platform. Lab: Integrating Applications with Google Cloud Storage. Google Container Engine Define the concept of a container and identify uses for containers. Identify the purpose of and use cases for Google Container Engine and Kubernetes. Introduction to Hybrid and Multi-Cloud computing (Anthos). Lab: Deploying Applications Using Google Container Engine. Google App Engine and Google Cloud Datastore Understand the purpose of and use cases for Google App Engine and Google Cloud Datastore. Contrast the App Engine Standard environment with the App Engine Flexible environment. Understand the purpose of and use cases for Google Cloud Endpoints. Lab: Deploying Applications Using App Engine and Cloud Datastore. Deployment and Monitoring Understand the purpose of template-based creation and management of resources. Understand the purpose of integrated monitoring, alerting, and debugging. Lab: Getting Started with Stackdriver and Deployment Manager. Big Data and Machine Learning Understand the purpose of and use cases for the products and services in the Google Cloud big data and machine learning platforms. Lab: Getting Started with BigQuery. Summary and Review Summary and Review. What's Next?.

Duration 5 Days 30 CPD hours This course is intended for Linux system administrators, virtualization administrators, and hybrid infrastructure engineers interested in deploying large-scale virtualization solutions and managing virtual servers in their datacenters, based on the Red Hat Virtualization open virtualization management platform. Overview As a result of completing this offering, you should be able to create and deploy Red Hat Virtualization and virtual servers. Using a single, full-service management interface, Red Hat Virtualization Manager, you will be able to configure, manage, and migrate systems within the virtualization environment. In this course you will develop the skills needed to deploy, administer, and operate virtual machines in your organization using Red Hat© Virtualization. Through numerous hands-on exercises, you will demonstrate the ability to deploy and configure the Red Hat Virtualization infrastructure and use it to provision and manage virtual machines. This offering also prepares you for the Red Hat Certified Specialist in Virtualization exam.This course is based on Red Hat Enterprise Virtualization 4.3 and Red Hat Enterprise Linux© 7.6 and 8, as well as Red Hat Hyperconverged Infrastructure for Virtualization 1.6.This course covers the same material as RH318, but includes the Red Hat Certified Specialist in Virtualization exam (EX318). Red Hat Virtualization overview Explain the purpose and architecture of Red Hat Virtualization. Install and configure Red Hat Virtualization Install a minimal Red Hat Virtualization environment and use it to create a virtual machine. Create and manage datacenters and clusters Organize hypervisors into groups using datacenters and clusters. Manage user accounts and roles Configure user accounts using a central directory service, then use roles to assign access to resources based on job responsibilities. Adding physical hosts Add additional Red Hat Virtualization hosts automatically, and move and remove hosts from datacenters as needed. Scale Red Hat Virtualization infrastructure Add Red Hat Virtualization hosts automatically, configure Red Hat Enterprise Linux hosts when appropriate, and move and remove hosts from data centers as needed. Manage Red Hat Virtualization networks Separate network traffic into multiple networks on one or more interfaces to improve the performance and security of Red Hat Virtualization. Manage Red Hat Virtualization storage Create and manage data and ISO storage domains. Deploy and manage virtual machines Operate virtual machines in the Red Hat Virtualization environment. Migrate virtual machines Migrate and control automatic migration of virtual machines. Manage virtual machine images Manage virtual machine snapshots and disk images. Automating virtual machine deployment Automate deployment of virtual machines by using templates and cloud-init. Back up and upgrade Red Hat Virtualization Back up, restore, and upgrade the software in a Red Hat Virtualization environment. Explore high-availability practices Explain procedures to improve the resilience and reliability of Red Hat Virtualization by removing single points of failure and implementing high-availability features. Perform comprehensive review Demonstrate skills learned in this course by installing and configuring Red Hat Virtualization; using the platform to create and manage virtual machines; and backing up and updating components of Red Hat Virtualization.

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Duration 1 Days 6 CPD hours This course is intended for This class is intended for the following: Data analysts, Data scientists, Business analysts getting started with Google Cloud Platform. Individuals responsible for designing pipelines and architectures for data processing, creating and maintaining machine learning and statistical models, querying datasets, visualizing query results and creating reports. Executives and IT decision makers evaluating Google Cloud Platform for use by data scientists. Overview This course teaches students the following skills:Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform.Use Cloud SQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform.Employ BigQuery and Cloud Datalab to carry out interactive data analysis.Train and use a neural network using TensorFlow.Employ ML APIs.Choose between different data processing products on the Google Cloud Platform. This course introduces participants to the Big Data and Machine Learning capabilities of Google Cloud Platform (GCP). It provides a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities. Introducing Google Cloud Platform Google Platform Fundamentals Overview. Google Cloud Platform Big Data Products. Compute and Storage Fundamentals CPUs on demand (Compute Engine). A global filesystem (Cloud Storage). CloudShell. Lab: Set up a Ingest-Transform-Publish data processing pipeline. Data Analytics on the Cloud Stepping-stones to the cloud. Cloud SQL: your SQL database on the cloud. Lab: Importing data into CloudSQL and running queries. Spark on Dataproc. Lab: Machine Learning Recommendations with Spark on Dataproc. Scaling Data Analysis Fast random access. Datalab. BigQuery. Lab: Build machine learning dataset. Machine Learning Machine Learning with TensorFlow. Lab: Carry out ML with TensorFlow Pre-built models for common needs. Lab: Employ ML APIs. Data Processing Architectures Message-oriented architectures with Pub/Sub. Creating pipelines with Dataflow. Reference architecture for real-time and batch data processing. Summary Why GCP? Where to go from here Additional Resources Additional course details: Nexus Humans Google Cloud Platform Big Data and Machine Learning Fundamentals training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Google Cloud Platform Big Data and Machine Learning Fundamentals course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 1 Days 6 CPD hours This course is intended for This course is intended for: Data platform engineers Architects and operators who build and manage data analytics pipelines Overview In this course, you will learn to: Compare the features and benefits of data warehouses, data lakes, and modern data architectures Design and implement a batch data analytics solution Identify and apply appropriate techniques, including compression, to optimize data storage Select and deploy appropriate options to ingest, transform, and store data Choose the appropriate instance and node types, clusters, auto scaling, and network topology for a particular business use case Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights Secure data at rest and in transit Monitor analytics workloads to identify and remediate problems Apply cost management best practices In this course, you will learn to build batch data analytics solutions using Amazon EMR, an enterprise-grade Apache Spark and Apache Hadoop managed service. You will learn how Amazon EMR integrates with open-source projects such as Apache Hive, Hue, and HBase, and with AWS services such as AWS Glue and AWS Lake Formation. The course addresses data collection, ingestion, cataloging, storage, and processing components in the context of Spark and Hadoop. You will learn to use EMR Notebooks to support both analytics and machine learning workloads. You will also learn to apply security, performance, and cost management best practices to the operation of Amazon EMR. Module A: Overview of Data Analytics and the Data Pipeline Data analytics use cases Using the data pipeline for analytics Module 1: Introduction to Amazon EMR Using Amazon EMR in analytics solutions Amazon EMR cluster architecture Interactive Demo 1: Launching an Amazon EMR cluster Cost management strategies Module 2: Data Analytics Pipeline Using Amazon EMR: Ingestion and Storage Storage optimization with Amazon EMR Data ingestion techniques Module 3: High-Performance Batch Data Analytics Using Apache Spark on Amazon EMR Apache Spark on Amazon EMR use cases Why Apache Spark on Amazon EMR Spark concepts Interactive Demo 2: Connect to an EMR cluster and perform Scala commands using the Spark shell Transformation, processing, and analytics Using notebooks with Amazon EMR Practice Lab 1: Low-latency data analytics using Apache Spark on Amazon EMR Module 4: Processing and Analyzing Batch Data with Amazon EMR and Apache Hive Using Amazon EMR with Hive to process batch data Transformation, processing, and analytics Practice Lab 2: Batch data processing using Amazon EMR with Hive Introduction to Apache HBase on Amazon EMR Module 5: Serverless Data Processing Serverless data processing, transformation, and analytics Using AWS Glue with Amazon EMR workloads Practice Lab 3: Orchestrate data processing in Spark using AWS Step Functions Module 6: Security and Monitoring of Amazon EMR Clusters Securing EMR clusters Interactive Demo 3: Client-side encryption with EMRFS Monitoring and troubleshooting Amazon EMR clusters Demo: Reviewing Apache Spark cluster history Module 7: Designing Batch Data Analytics Solutions Batch data analytics use cases Activity: Designing a batch data analytics workflow Module B: Developing Modern Data Architectures on AWS Modern data architectures

Duration 3 Days 18 CPD hours This course is intended for Individuals involved in IT development, IT operations or IT service management; Those whose role is touched by DevOps and continuous delivery, such as the following IT roles: DevOps engineers, Product owners Integration specialists, Operations managers, Incident & change managers, System administrators, Network administrators, Business managers, Automation architects, Enterprise architects, Testers Overview Know the emergence of DevOps Know the core concepts and principles of DevOps Know what DevOps means for you as professional and for your organization Know the essence of a DevOps culture Understand the key elements of a DevOps culture Know the important aspects when creating a DevOps culture Know the Operational models of DevOps Understand the need for autonomous teams Understand the impact of DevOps on Architecture with respect to deployment Understand governance within DevOps teams Understand Agile, Scrum and Kanban and how these practices relate to one another Understand how ITSM processes relate to practices in a DevOps culture Understand how lean is used to optimise processes Know how to provide a Value Stream Map for a given process Understand the way to harvest new and innovative ideas Know the impact of automation on Software Delivery processes Understand the benefits and core principles of Continuous Delivery Describe the key cloud principles for DevOps organisations Know the relevance of monitoring and logging DevOps This course is designed to provide the core education necessary to build your DevOps vocabulary and to understand its principles and practices. With the help of key DevOps concepts and terminology, real-life case studies, examples and interactive group discussions and extensive exercises in each module you will acquire a fundamental understanding of DevOps. Introduction Let?s Get to Know Each Other Overview Course Objectives Mapping of the Competence Model with the Course Modules Course Agenda Type of Activities Exam Course Book Technical Glossary Group Activity Module Summary DevOps Introduction Module Objectives Module Topics Emergence of DevOps Core Concepts of DevOps DevOps Agile Skills Association (DASA) Module Summary Module End Questions Culture Module Objectives Module Topics Essence of a DevOps Culture Key Elements of DevOps Implementation of a DevOps Culture Module Summary Module End Questions Organization Module Objectives Module Topics Organizational Model Autonomous Teams Architecting for DevOps Governance Module Summary Module End Questions Processes Module Objectives Module Topics Process Basics DevOps in Relation to ITSM Agile and Scrum 12 Principles of the Agile Manifesto Optimizing Processes Using Lean Business Value Optimization and Business Analysis Using Story Mapping Module Summary Module End Questions Automation Module Objectives 6A Automation Concepts Automation for Delivery of Software Continuous Delivery Core Concepts Continuous Delivery Automation Concepts Continuous Delivery Automation Focus Topics 6B Data Center Automation Emergence of Cloud Technology and Principles Cloud Services Concepts in a DevOps Organization Automated Provisioning Concepts Platform Product Characteristics and Application Maturity Module Summary Module End Questions Measure and Improvement Module Objectives Module Topics Importance of Measurement Choosing the Right Metrics Monitoring and Logging Module Summary Module End Questions

This programme concentrates on the core planning skills needed to develop sound practical project plans in a team environment. This enables the plan to be modified should requirements change or difficulties arise. The programme also gives participants the confidence to practise those skills and apply them in the work environment and deliver their projects more successfully in the future. Participants learn fundamental project management concepts and terminology, demystifying the project management process, and, in particular, how to: Break a project down into manageable sections and ensure nothing is left out Understand and apply estimating techniques to develop realistic estimates Sequence work effectively and carry out critical path analysis to determine project duration and which tasks to pay closest attention to Manage project risk effectively to protect project value Monitor, control and re-plan the project to best keep it on track Close out the project and ensure the project comes to an orderly end 1 Introduction Self-introductions and personal objectives Course objectives Sharing of project issues 2 Project management concepts Characteristics of a project and what should be kept as operational responsibilities Understanding the triple and quadruple constraints - and their limitations Prioritising requirements through the MOSCOW technique Product v project life cycle Key project roles and responsibilities - the importance of sponsorship and clarity of roles 3 Starting a project, and the importance of the terms of reference / project brief Avoiding the pressure to 'just do it'! The importance and benefits of planning The best time to learn! Initial project documentation - the BOSCARDI approach 4 Breaking the work down Understanding alternative breakdown structures such as the product breakdown structure and work breakdown structure Guidelines for creating a work breakdown structure to ensure the full work scope is identified 5 Estimating Alternative estimating techniques and associated confidence levels Further considerations - loss and resource factors 6 Organising the work Use of network diagrams to develop a clear sequence of work Critical path analysis and calculating the project duration and task float - and usage 7 The management of project risk Understanding the nature of project risk The risk analysis and risk management processes How to best manage threats and opportunities Running a risk workshop Using the risk register 8 Scheduling the work The importance of the Gantt chart and understanding its limitations The Gantt chart layout and using alternative views such as the tracking Gantt Using alternative dependencies 9 Resource issues Assigning resources and resolving resource overloads Crashing and fast-tracking your project and potential issues to look out for 10 Controlling the project The control cycle and alternative feedback mechanisms Alternative progress reporting Assessing the impact The importance of re-planning The benefits of control Change control - the importance of impact analysis The steps of change control and the use of the issue register 11 Closing the project The project closure checklist Reviewing the project - things to avoid Developing meaningful lessons and ensuring they are applied effectively The post-project review - its importance to the organisation

An effective Access Control System can form an integral part of an effective security system. At Hi-Tech Training our course is designed to give participants a practical knowledge of the operation and installation of Access Control Systems. Our experience has taught us that in order to gain the required skills an installer needs to learn through as much practical training as possible. This course involves 50% “Hands-On” training which involves building, setting up, testing and troubleshooting faults using core elements of modern Access Control Systems. At the end of the course, an interested and hardworking participant will have a good solid foundation of knowledge of what access control is all about.

Cloud deployment training course description This course covers the important topics every cloud professional needs, including, configuration and deployment, security, maintenance, management, and troubleshooting. It covers all aspects of cloud computing infrastructure and administration, with a practical focus on real-world skills. It will help you to master the fundamental concepts, terminology, and characteristics of cloud computing. Deploy and implement cloud solutions, manage the infrastructure, and monitor performance. You will also be able to install, configure, and manage virtual machines. What will you learn Cloud services, models, and characteristics. Virtualization components, installation, and configuration. Infrastructure configurations and optimization. Resource management and specific allocations. IT security concepts, tools, and best practices. Recovery, availability and continuity in the cloud. Cloud deployment training course details Who will benefit: IT professionals looking to deploy and implement cloud solutions, manage the infrastructure, and monitor performance, Install, configure, and manage virtual machines. Prerequisites: Introduction to virtualization. Duration 5 days Cloud deployment training course contents Preparing to Deploy Cloud Solutions Deploying a Pilot Project Testing Pilot Project Deployments Designing a Secure and Compliant Cloud Infrastructure Designing and Implementing a Secure Cloud Environment Planning Identity and Access Management for Cloud Deployments Determining CPU and Memory Sizing for Cloud Deployments Determining Storage Requirements for Cloud Deployments Analysing Workload Characteristics to Ensure Successful Migration Maintaining Cloud Systems Implementing Backup, Restore, Disaster Recovery, and Business Continuity Measures Analysing Cloud Systems for Performance Analysing Cloud Systems for Anomalies and Growth Forecasting Troubleshooting Deployment, Capacity, Automation, and Orchestration Issues Troubleshooting Connectivity Issues Troubleshooting Security Issues