287 Courses

Duration 5 Days 30 CPD hours This course is intended for Software Engineers and DevOps professionals working in an Enterprise developing mission critical line of business applications. Overview By the end of this course, students will be able to: Assess the advantages of a containerized software development & deployment Use Docker engine features necessary for running containerized applications Utilize Swarm and Kubernetes orchestrators to deploy, maintain, and scale a distributed application Describe the essential patterns used in a highly distributed EE application Understand how to configure EE applications for different environments without code changes Produce and containerize scalable, accessible, and fault-tolerant EE applications Apply different debugging and testing techniques to containerized EE applications The Docker Fundamentals + Enterprise Operations Bundle includes the full Docker for Enterprise Operations course as well as the prerequisite Docker Fundamentals course run back to back in a single intensive training experience. The Docker Fundamentals training course features the foundational concepts and practices of containerization on a single Docker node. The course offers learners the opportunity to assimilate basic container orchestration and how to scale Docker across multiple nodes in a simple swarm cluster. This course provides essential foundational knowledge for subsequent Docker courses. As the follow-on to the Docker Fundamentals course, Docker for Enterprise Operations is a role-based course is designed for Docker Operations teams to accelerate their Docker journey in the enterprise. The course covers in-depth core advanced features of Docker EE and best practices to apply these features at scale with enterprise workloads. Platform Availability: Linux, Windows (Fundamentals Only). Introducing Docker Containerization Fundamentals Creating Images Docker Volumes Docker Networking Basics Introduction to Docker Compose Introduction to Swarm Mode Introduction to Kubernetes Secrets Fundamentals Signature Assignment Distributed Application Architecture Sample Application Edit and Continue Debugging Docker Compose Testing Service Discovery Defensive Programming Logging and Error Handling Health Checks Secrets Configuration Management Development Pipeline Overview Universal Control Plane Docker Trusted Registry Repository Automation Build Server

Duration 2 Days 12 CPD hours This course is intended for Develop for experienced IT Professionals familiar with Citrix Virtual Apps and Desktops 7.1x in an on-premises environment. Potential students include administrators, engineers, and architects designing or deploying Citrix Virtual Apps and Desktops workloads on Microsoft Azure. Overview Prepare the Azure environment for secure integration with Citrix Virtual Apps and Desktops Deploy and manage Virtual Delivery Agent machines in Microsoft Azure using Machine Creation Services Integrate Citrix Cloud and Citrix Virtual Apps and Desktops with Microsoft Azure Active Directory Design Machine Catalogs and virtual machines on Microsoft Azure Resource Manager Provide remote access with Citrix StoreFront and Citrix Gateway on Microsoft Azur Students learn to deploy and manage the Citrix Virtual Apps and Desktops apps and desktops in Microsoft Azure. Students gain the skills to plan the machine catalog and virtual machine design based in Microsoft?s public cloud and get hands-on practice deploying those machines using Machine Creation Services. Students will also learn about additional Azure considerations including maintenance and power management which are critical in a cloud environment. For remote access, students will learn to configure Citrix StoreFront and Citrix Gateway on the Azure platform. This course focuses on Microsoft Azure as a Citrix Cloud resource location however concepts are relevant to both Citrix Cloud and fully managed Citrix Virtual Apps and Desktops sites. Citrix Virtual Apps and Desktops on Azure Overview Defining IAAS Citrix Virtual Apps and Desktops Azure Deployment Models Azure Fundamentals Review Azure Management Azure Locations Citrix Virtual Apps and Desktops Integration with Azure Active Directory Active Directory Basics Active Directory Usage Connecting On-premises Active Directory to Azure Azure Role Based Access Control Connecting to Microsoft Azure Azure Connectivity Cloud Connectors in Azure Creating Citrix Virtual Apps and Desktops Host Connections to Azure Deploying Apps and Desktops using Machine Creation Services Master Image Preparation Machine Creation Services in Azure Considerations for Deploying onto Azure Providing Access to End Users StoreFront Locations Citrix ADC Locations Multiple Citrix Virtual Apps and Desktops Zones in Azure Regions Maintaining Infrastructure and VDAs in Microsoft Azure Maintaining Infrastructure Maintaining Resources Power Management Plan for a Successful POC Planning your next steps

Duration 4 Days 24 CPD hours This course is intended for Anyone who is preparing to build and run Kubernetes clusters Overview By the end of the course, you should be able to meet the following objectives: Build, test, and publish Docker container images Become familiar with YAML files that define Kubernetes objects Understand Kubernetes core user-facing concepts, including pods, services, and deployments Use kubectl, the Kubernetes CLI, and become familiar with its commands and options Understand the architecture of Kubernetes (Control plane and its components, worker nodes, and kubelet) Learn how to troubleshoot issues with deployments on Kubernetes Apply resource requests, limits, and probes to deployments Manage dynamic application configuration using ConfigMaps and Secrets Deploy other workloads, including DaemonSets, Jobs, and CronJobs Learn about user-facing security using SecurityContext, RBAC, and NetworkPolicies This four-day course is the first step in learning about Containers and Kubernetes Fundamentals and Cluster Operations. Through a series of lectures and lab exercises, the fundamental concepts of containers and Kubernetes are presented and put to practice by containerizing and deploying a two-tier application into Kubernetes. Course Introduction Introductions and objectives Containers What and Why containers Building images Running containers Registry and image management Kubernetes Overview Kubernetes project Plugin interfaces Building Kubernetes Kubectl CLI Beyond Kubernetes Basics Kubernetes objects YAML Pods, replicas, and deployments Services Deployment management Rolling updates Controlling deployments Pod and container configurations Kubernetes Networking Networking within a pod Pod-to-Pod Networking Services to Pods ClusterIP, NodePort, and LoadBalancer Ingress controllers Service Discovery via DNS Stateful Applications in Kubernetes Stateless versus Stateful Volumes Persistent volumes claims StorageClasses StatefulSets Additional Kubernetes Considerations Dynamic configuration ConfigMaps Secrets Jobs, CronJobs Security Network policy Applying a NetworkPolicy SecurityContext runAsUser/Group Service accounts Role-based access control Logging and Monitoring Logging for various objects Sidecar logging Node logging Audit logging Monitoring architecture Monitoring solutions Octant VMware vRealize Operations Manager Cluster Operations Onboarding new applications Backups Upgrading Drain and cordon commands Impact of an upgrade to running applications Troubleshooting commands VMware Tanzu portfolio overview

Duration 4 Days 24 CPD hours This course is intended for Data center engineers Engineers (design, implementation, pre-sales, post-sales) Product managers and sales Overview After taking this course, you should be able to: Describe hyperconvergence, Cisco HyperFlex, and the components of Cisco HyperFlex Explain the Cisco Unified Computing System™ (Cisco UCS) and what makes it valuable to business Describe how Cisco HyperFlex Data Platform (HXDP) works Describe the physical components of Cisco HyperFlex Describe Cisco Intersight and introduce functionalities relevant to HyperFlex Install standard ESXi-based vSphere Cisco HyperFlex Manage your Cisco HyperFlex VMware ESXi-based cluster Describe how to maintain Cisco HyperFlex Design a Cisco HyperFlex solution Protect the data on your Cisco HyperFlex cluster using replication and data at rest encryption Describe a stretched cluster and how is it different from a standard cluster Describe an Edge cluster and how is it different from a standard cluster Perform basic troubleshooting tasks and explain Cisco Intersight The Implementing Cisco HyperFlex (DCIHX) v1.3 course shows you how to deploy and use the Cisco© HyperFlex? data platform to support multicloud workloads. You will become familiar with HyperFlex components and learn how to install, design, manage, and troubleshoot Cisco HyperFlex to support highly scalable and resilient multicloud implementations. You will also gain hands-on experience focused on installation, management, maintenance, and native replication, and you will explore cluster technologies as well as Cisco Intersight.? Introducing Hyperconvergence and Cisco HyperFlex Traditional Data Center Design What Is Hyperconvergence? Describing Cisco UCS: The Foundation of Cisco HyperFlex Cisco Server Deployment Models: Standalone Versus Managed Cisco UCS Managed Model Benefits Describing Cisco HyperFlex Software Components Virtual Machine Hypervisor Log-Structured File System Describing Cisco HyperFlex Hardware Components Introducing Cisco HyperFlex Servers Storage Technologies in Cisco HyperFlex Introducing Cisco Intersight Introducing Cisco Intersight Installing and Expanding Standard ESXi Cisco HyperFlex Installation Summary Software Prerequisites Managing Cisco HyperFlex in vSphere Environment Management Interfaces Overview Cisco HyperFlex Plugin for vCenter Maintaining Cisco HyperFlex Cisco HyperFlex Upgrade Overview Cisco HyperFlex Online Upgrade Designing Cisco HyperFlex Cluster Resiliency: VM-Level Cluster Resiliency: HXDP-Level Protecting Your Data Disaster Recovery Overview Third-Party Data Restore Solutions Introducing Cisco HyperFlex Stretched Deployment Stretched Cluster Overview Prerequisites Introducing Cisco HyperFlex EDGE Cisco HyperFlex EDGE Cluster Overview Prerequisites and Recommendations Troubleshooting Cisco HyperFlex Troubleshooting Guidelines Generating Tech Support Bundles

Duration 3 Days 18 CPD hours This course is intended for Data platform engineers Database administrators Solutions architects IT professionals Overview Apply database concepts, database management, and data modeling techniques Evaluate hosting databases on Amazon EC2 instances Evaluate relational database services (Amazon RDS, Amazon Aurora, and Amazon Redshift) and their features Evaluate nonrelational database services (Amazon DocumentDB, Amazon DynamoDB, Amazon ElastiCache, Amazon Neptune, and Amazon QLDB) and their features Examine how the design criteria apply to each service Apply management principles based on the unique features of each service This course will teach you the process of planning and designing both relational and nonrelational database and the planning and design requirements of all 8 of the AWS databases services, their pros and cons, and how to know which AWS databases service is right for your workloads. Day 1 Module 0: Planning and Designing Databases on AWS Module 1: Database Concepts and General Guidelines Module 2: Database Planning and Design Module 3: Databases on Amazon EC2 Module 4: Purpose-Built Databases Module 5: Databases on Amazon RDS Databases in Amazon Aurora Day 2 Module 6: Databases in Amazon Aurora (continued) Module 7: Databases in Amazon DocumentDB (with MongoDB compatibility) Module 8: Amazon DynamoDB Tables Day 3 Module 9: Databases in Amazon Neptune Module 10: Databases in Amazon Quantum Ledger Database (Amazon QLDB) Module 11: Databases in Amazon ElastiCache Module 12: Data Warehousing in Amazon Redshift Module 13: Course Review Additional course details: Nexus Humans Planning and Designing Databases on AWS 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 Planning and Designing Databases on AWS 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 3 Days 18 CPD hours This course is intended for Cluster administrators (Junior systems administrators, junior cloud administrators) interested in deploying additional clusters to meet increasing demands from their organizations. Cluster engineers (Senior systems administrators, senior cloud administrators, cloud engineers) interested in the planning and design of OpenShift clusters to meet performance and reliability of different workloads and in creating work books for these installations. Site reliability engineers (SREs) interested in deploying test bed clusters to validate new settings, updates, customizations, operational procedures, and responses to incidents. Overview Validate infrastructure prerequisites for an OpenShift cluster. Run the OpenShift installer with custom settings. Describe and monitor each stage of the OpenShift installation process. Collect troubleshooting information during an ongoing installation, or after a failed installation. Complete the configuration of cluster services in a newly installed cluster. Installing OpenShift on a cloud, virtual, or physical infrastructure. Red Hat OpenShift Installation Lab (DO322) teaches essential skills for installing an OpenShift cluster in a range of environments, from proof of concept to production, and how to identify customizations that may be required because of the underlying cloud, virtual, or physical infrastructure. This course is based on Red Hat OpenShift Container Platform 4.6. 1 - Introduction to container technology Describe how software can run in containers orchestrated by Red Hat OpenShift Container Platform. 2 - Create containerized services Provision a server using container technology. 3 - Manage containers Manipulate prebuilt container images to create and manage containerized services. 4 - Manage container images Manage the life cycle of a container image from creation to deletion. 5 - Create custom container images Design and code a Dockerfile to build a custom container image. 6 - Deploy containerized applications on OpenShift Deploy single container applications on OpenShift Container Platform. 7 - Troubleshoot containerized applications Troubleshoot a containerized application deployed on OpenShift. 8 - Deploy and manage applications on an OpenShift cluster Use various application packaging methods to deploy applications to an OpenShift cluster, then manage their resources. 9 - Design containerized applications for OpenShift Select a containerization method for an application and create a container to run on an OpenShift cluster. 10 - Publish enterprise container images Create an enterprise registry and publish container images to it. 11 - Build applications Describe the OpenShift build process, then trigger and manage builds. 12 - Customize source-to-image (S2I) builds Customize an existing S2I base image and create a new one. 13 - Create applications from OpenShift templates Describe the elements of a template and create a multicontainer application template. 14 - Manage application deployments Monitor application health and implement various deployment methods for cloud-native applications. 15 - Perform comprehensive review Create and deploy cloudinative applications on OpenShift.

Duration 1 Days 6 CPD hours This course is intended for This course is intended for: Developers responsible for developing Deep Learning applications Developers who want to understand concepts behind Deep Learning and how to implement a Deep Learning solution on AWS Overview This course is designed to teach you how to: Define machine learning (ML) and deep learning Identify the concepts in a deep learning ecosystem Use Amazon SageMaker and the MXNet programming framework for deep learning workloads Fit AWS solutions for deep learning deployments In this course, you?ll learn about AWS?s deep learning solutions, including scenarios where deep learning makes sense and how deep learning works. You?ll learn how to run deep learning models on the cloud using Amazon SageMaker and the MXNet framework. You?ll also learn to deploy your deep learning models using services like AWS Lambda while designing intelligent systems on AWS. Module 1: Machine learning overview A brief history of AI, ML, and DL The business importance of ML Common challenges in ML Different types of ML problems and tasks AI on AWS Module 2: Introduction to deep learning Introduction to DL The DL concepts A summary of how to train DL models on AWS Introduction to Amazon SageMaker Hands-on lab: Spinning up an Amazon SageMaker notebook instance and running a multi-layer perceptron neural network model Module 3: Introduction to Apache MXNet The motivation for and benefits of using MXNet and Gluon Important terms and APIs used in MXNet Convolutional neural networks (CNN) architecture Hands-on lab: Training a CNN on a CIFAR-10 dataset Module 4: ML and DL architectures on AWS AWS services for deploying DL models (AWS Lambda, AWS IoT Greengrass, Amazon ECS, AWS Elastic Beanstalk) Introduction to AWS AI services that are based on DL (Amazon Polly, Amazon Lex, Amazon Rekognition) Hands-on lab: Deploying a trained model for prediction on AWS Lambda Additional course details: Nexus Humans Deep Learning on AWS 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 Deep Learning on AWS 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 5 Days 30 CPD hours This course is intended for Professionals who need to maintain or set up a Kubernetes cluster Container Orchestration Engineers DevOps Professionals Overview Cluster architecture, installation, and configuration Rolling out and rolling back applications in production Scaling clusters and applications to best use How to create robust, self-healing deployments Networking configuration on cluster nodes, services, and CoreDNS Persistent and intelligent storage for applications Troubleshooting cluster, application, and user errors Vendor-agnostic cloud provider-based Kubernetes Kubernetes is a Cloud Orchestration Platform providing reliability, replication, and stability while maximizing resource utilization for applications and services. By the conclusion of this hands-on, vendor agnostic training you will go back to work with the knowledge, skills, and abilities to design, implement, and maintain a production-grade Kubernetes cluster. We prioritize covering all objectives and concepts necessary for passing the Certified Kubernetes Administrator (CKA) exam. You will be provided the components necessary to assemble your own high availability Kubernetes environment and configure, expand, and control it to meet the demands made of cluster administrators. Your week of intensive, hands-on training will conclude with a mock CKA exam that simulates the real exam. Cluster Architecture, Installation & Configuration Each student will be given an environment that allows them to build a Kubernetes cluster from scratch. After a detailed discussion on key architectural components and primitives, students will install and compare two production grade Kubernetes clusters. Review: Kubernetes Fundamentals After successfully instantiating their own Kubernetes Cluster, students will be guided through foundational concepts of deploying and managing applications in a production environment. Workloads & Scheduling After establishing a solid Kubernetes command line foundation, students will be led through discussion and hands-on labs which focus on effectively creating applications that are easy to configure, simple to manage, quick to scale, and able to heal themselves. Services & Networking Thoroughly understanding the underlying physical and network infrastructure of a Kubernetes cluster is an essential skill for a Certified Kubernetes Administrator. After an in-depth discussion of the Kubernetes Networking Model, students explore the networking of their cluster?s Control Plane, Workers, Pods, and Services. Storage Certified Kubernetes Administrators are often in charge of designing and implementing the storage architecture for their clusters. After discussing many common cluster storage solutions and how to best use each, students practice incorporating stateful storage into their applications. Troubleshooting A Certified Kubernetes Administrator is expected to be an effective troubleshooter for their cluster. The lecture covers a variety of ways to evaluate and optimize available log information for efficient troubleshooting, and the labs have students practice diagnosing and resolving several typical issues within their Kubernetes Cluster. Certified Kubernetes Administrator Practice Exam Just like the Cloud Native Computing Foundation CKA Exam, the students will be given two hours to complete hands-on tasks in their own Kubernetes environment. Unlike the certification exam, students taking the Alta3 CKA Practice Exam will have scoring and documented answers available immediately after the exam is complete, and will have built-in class time to re-examine topics that they wish to discuss in greater depth.

Duration 3.5 Days 21 CPD hours This course is intended for This course is for AWS Cloud Architects with expertise in designing and implementing solutions running on AWS who now want to design for Microsoft Azure. Overview After completing this course, students will be able to: Secure identities with Azure Active Directory and users and groups. Implement identity solutions spanning on-premises and cloud-based capabilities Apply monitoring solutions for collecting, combining, and analyzing data from different sources. Manage subscriptions, accounts, Azure policies, and Role-Based Access Control. Administer Azure using the Resource Manager, Azure portal, Cloud Shell, and CLI. Configure intersite connectivity solutions like VNet Peering, and virtual network gateways. Administer Azure App Service, Azure Container Instances, and Kubernetes. This course teaches Solutions Architects who have previously designed for Amazon Web Services how to translate business requirements into secure, scalable, and reliable solutions for Azure. Introduction to Azure Subscriptions and accounts Resource groups and templates in Azure Resource Manager Azure global infrastructure Azure regions Azure Availability Zones Comparison with AWS Implement Azure Active Directory Introduction to Azure Active Directory Domains and custom domains Safety features Guest users in Azure Active Directory Manage multiple directories Comparison with AWS Implement and manage hybrid identities Introduction to Azure AD Connect Comparison with AWS Implement virtual networking Azure Virtual Network and VNet peering VPN and ExpressRoute connections Comparison with AWS Implement VMs for Windows and Linux Configure high availability Comparison with AWS Implement load balancing and network security Implement Azure Load Balancer Implement an Azure Application Gateway Implement Azure Firewall Implement network security groups and application security groups Comparison with AWS Implement container-based applications Configure Azure Kubernetes Service Publish a solution on an Azure Container Instance Comparison with AWS Implement an application infrastructure Create an App Service plan Create and configure Azure App Service Configure networking for an App Service Introduction to Logic Apps and Azure Functions Comparison with AWS Implement storage accounts Azure Storage core concepts Managing the Azure Blob storage lifecycle Working with Azure Blob storage Comparison with AWS Implement NoSQL databases Introduction to Azure Cosmos DB Consistency Select appropriate CosmosDB APIs Set up replicas in CosmosDB Comparison with AWS DynamoDB Implement Azure SQL databases Configure Azure SQL database settings Implement Azure SQL Database managed instances Configure high availability for an Azure SQL database Comparison with AWS Implement cloud infrastructure monitoring Monitor security Monitor cost Configure a Log Analytics workspace Comparison with AWS Implement and manage Azure governance solutions Assign RBAC roles Configure management access to Azure Implement and configure an Azure Policy Comparison with AWS Manage security for applications Implement Azure Key Vault Implement and configure Azure AD Managed Identities Register and manage applications in Azure AD Comparison with AWS Migration, backup, and disaster recovery management Migrate workloads Implement Azure Backup for VMs Implement disaster recovery Comparison with AWS

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.