DevOps Engineer to Cloud Architect

Learners can explore the concept of DevOps and the DevOps principles and practices that can help eliminate the problems associated with traditional software development approaches in this 15-video course. You will examine different approaches for embracing the DevOps mindset, the key elements of the DevOps mindset, and the differences and similarities between DevOps and CloudOps. Then learn the essential principles and practices of DevOps that can be adopted in software development projects to eliminate the shortcomings of traditional software development approaches. Next, learn key elements of the DevOps mindset, and the roles of design thinking, Lean, and Agile in enabling and facilitating the DevOps mindset in the enterprise. Take a look at tools that can be used to set up and implement end-to-end DevOps and CloudOps practices and learn how to configure Bitbucket to implement code collaboration by using code repository, build, and automated deployment. Finally, you will explore the use of configuration management tools to manage deployments on diversified targets, including on-premises and in the cloud.

Explore the common application management roadblocks and the solutions provided by adopting DevOps practices and principles. Examine the concept of Disciplined DevOps and its workflow and how to set up deployment environments and release management solutions by using Atlassian tools and cloud containers. Key concepts covered in this 14-video course include how the adoption of DevOps practices and principles can help resolve roadblocks; the workflow of Disciplined DevOps, with a focus on how Disciplined DevOps addresses challenges faced by modern organizations; and paradigms used to provide management layers for delivering applications using DevOps. Next, you will learn the features of prominent tools and platforms used to set up traditional and DevOps deployment environments; set up deployment projects using Atlassian tools; and apply DevOps principles to setting up modern deployment environments that are powered by containerization in the cloud. Finally, learn about general release scheduling strategies that potentially support the Disciplined DevOps mindset, and set up release management solutions that can help track cross-project releases on a single board with custom release workflows.

Explore Kanban strategies for meeting project timelines, core practices used in Kanban, terms and goals that are critical for successful rollout of Kanban for operations, and key elements of Kanban for project management. Discover how to track and forecast projects using Kanban and how Waterfall teams can transition to Kanban. Key concepts in this 18-video course include the history of Kanban and strategies for helping IT operations teams meet project timelines; Kanban elements that are used to manage project execution; and how Kanban enables value stream visualization and ensures stable flow of DevOps practices. You will learn how to create Kanban boards and configure the columns to monitor project work and implement continuous delivery of work; use the Kanban project management approach to demonstrate Agile metrics for optimizing delivery; and configure Kanban boards and visualize the flow of map teams' workflow stages. Then see how to set work-in-progress (WIP) limits to constrain while using Kanban projects; apply Kanban approaches to manage DevOps practices; and learn quality control and continuous improvement processes that are applied in Kanban.

In this 15-video course, you will explore the objectives of Agile and the core principles based on the Agile Manifesto. Learners will examine the similarities and differences between Agile and DevOps, the combined benefits of Agile and DevOps in managing large-scale enterprise projects, and differences between Agile and DevOps practices. You will learn how to create Scrum boards to implement the Agile project management methodology and how to create, configure, and start Sprints to manage projects and backlogs. Next, take a look at Scrum roles used to implement Agile projects and examine the types of reports that can help track work in progress (WIP), including Burndown reports and velocity charts. Then learn about steps involved in the visual requirements-gathering process for Agile project management, before hearing about the influence of architecture vision on team velocity and software quality. Finally, you will discover how to set up and implement Agile product development projects and practices by using Jira, Azure DevOps, and TFS (Team Foundation Server).

Learners can explore key principles, tools, and foundational practices that are essential in implementing continuous delivery and continuous integration, and the role of architecture, culture, and patterns in ensuring their success, in this 22-video course. Become familiar with key principles and practices for successful continuous integration, and the differences between continuous integration, continuous delivery, continuous deployment, and value stream mapping. Learn how to set up, build, and implement continuous integration and continuous delivery workflows in Bitbucket pipelines by using sample Node.js application, Branch-per-issue, and Atlassian tools. Examine the Continuous Delivery Maturity Model, the components and benefits of Application Release Automation, and the criteria for selecting the right code repository solutions. Next, you will learn how to build GitFlow workflows to design branching models and enable scheduled release cycles, and about the use of GitLab as a complete DevOps platform for end-to-end DevOps lifecycle implementation. Finally, explore the use of CI/CD (continuous integration and continuous deployment) over containerization to drive down preproduction costs.

Use this 13-video course to explore the benefits of using Amazon Web Services (AWS) for implementing CloudOps and AWS services that can be used for end-to-end CloudOps lifecycle implementation. Learn which services are used to provision, configure, and manage AWS infrastructure resources using code and templates, while also monitoring and enforcing infrastructure compliance. You will learn how to set up continuous integration workflows with AWS CodePipeline; to build code with AWS CodeBuild; and to use CodeCommit to manage code repositories that can be used in CloudOps implementation. Explore how to use AWS CodeStar for application management, to develop, build, and deploy applications in AWS, and look at application deployment targets in AWS. Then take a look at implementing AWS CodePipeline in the AWS Cloud9 Integrated Development Environment, and how to work with AWS X-Ray to visualize application components, and identify and troubleshoot performance bottlenecks. Finally, learn how to build dynamic and enterprise-ready CI/CD (continuous integrated/continuous deployment) environments for serverless application deployment in AWS.

Explore features of prominent Azure DevOps and CloudOps tools that can be used for software development in this 14-video course, in which learners can discover how to install and configure Azure Boards for GitHub to manage the connection of Azure Board projects with GitHub repositories. Then, see how to create local Git repositories and synchronize them with centralized Git repositories in Azure DevOps. Examine how to use Azure Boards to manage software projects, configure CI/CD (continuous integration/continuous deployment) for applications using build and release in Azure Pipelines. You will discover how to work with Azure Artifacts to publish various packages in Azure DevOps, and use Azure DevOps to create test plans for validating applications to manage the software development project testing lifecycle. Explore performance monitoring with Applications Insight and deploying applications to AWS Cloud with Azure DevOps. The course concludes with a look at the approach of shifting security mindsets to DevSecOps culture with a focus on various security strategy components, and shows how to transform monolithic solutions into cloud solutions.

Explore the features, benefits, and solutions provided by Google Cloud Platform (GCP) for end-to-end CloudOps implementation in this 14-video course, which examines features of Cloud Build and how to work with Cloud Build to automate workflows and application deployment. Learn how to create a source code repository by using Google Cloud Console and gcloud command line tool. Also, learn how to build and implement delivery pipelines with tools and services provided by GCP by exploring the technical, process, measurement, and cultural capabilities of GCP that drive high software delivery with CloudOps, along with the prominent solutions, benefits, and approaches of implementing end-to-end GCP CloudOps. Examine features provided by GCP's Cloud Build, along with how to use Cloud Build and cloud source repositories and GitHub to automate App Engine deployment, the prominent tools provided by GCP, and how to build configuration management workflows in GCP. Finally, discover how to create continuous delivery pipelines by using Google Kubernetes Engine, cloud source repositories, Cloud Build, and Spinnaker for Google.

Examine how Infrastructure as Code (IaC) can eliminate deficiencies associated with manual resource configuration. Explore benefits and best practices for implementing IaC, prominent tools used, and benefits of Policy as Code. Key concepts covered in this 14-video course include how to use IaC to eliminate deficiencies associated with manual resource configuration; essential stages involved in the lifecycle of IaC and roles played by those stages; the transformation of IaC from DevOps to the CloudOps paradigm; and benefits of using Policy as Code to implement IaC. You will learn benefits and best practices for implementing IaC to realize CloudOps; prominent tools and services that can be used to implement IaC while applying the required DevOps and CloudOps policies; and the concept of Continuous Configuration Automation and tools used to automate and configure various layers of infrastructure. Next, see how to install Chef and write cookbooks that can be used to manage provisioning of resources; learn Infrastructure as a Service (IaaS) and differentiate between IaaS and IaC; and implement IaC with CloudFormation.

Final Exam: DevOps Engineer will test your knowledge and application of the topics presented throughout the DevOps Engineer track of the Skillsoft Aspire DevOps Engineer to Cloud Architect Journey.

In this 14-video course, learners will explore potential applications that can be automated, along with important advantages, disadvantages, and best practices of automation. You will examine how to develop enterprise automation capabilities that can help deliver controlled self-service capabilities for managing IT processes and infrastructure. First, learn about the key processes necessary to implement effective IT automation strategy, the key principles of DevOps, and the approach of automating the DevOps process by using integration technologies. Then explore the role of automation in DevOps and the benefits of enabling standardization in DevOps automation, along with the role and responsibilities of the DevOps Automation Architect. Identify the benefits of cloud automation and common cloud tasks that are a good candidate for automation. You will go on to learn about the tools used to automate DevOps practices; how to install and configure SaltStack to automate infrastructure provisioning; and how to automate static site deployment with AWS (Amazon Web Services) CloudFormation.

Discover how to implement and improve DevOps practices, tools that can be used across various development and operations processes, and how to set up DevOps architecture to improve the delivery of value. Explore essential DevOps and CloudOps design patterns used to enable DevOps and CloudOps in the enterprise, how to implement continuous deployment, and how to scale applications using cloud services. Examine the DevOps Operational Model, essential cloud design patterns that can be applied when building cloud-based applications to achieve continuous operations in public and private clouds, and critical patterns for implementing automation in DevOps and CloudOps workflows. How to build scalable architecture in AWS and how to design applications by applying the Runtime Reconfiguration pattern is also covered.

Explore automation approaches, how to work with the tools and services provided by AWS (Amazon Web Services) to implement DevOps and CloudOps in the enterprise, and their associated challenges, in this 18-video course. You will learn about critical automation patterns for implementing AWS DevOps and CloudOps; the effective approaches for automating IT processes using the AWS DevOps Reference Architecture; and the DevOps Transformation Assessment and Strategy. Additionally, you will discover how to use essential AWS Serverless Architecture patterns in automating IT processes and the prominent containerization services provided by AWS for CI/CD (continuous integration/continuous deployment). Learners will examine how to use and work with serverless architecture, AWS Lambda, AWS OpsWorks, AWS Systems Manager, and AWS Config to automate CI/CD pipelines. The course concludes with demonstrations of how to implement configuration management; how to manage EC2 (Amazon Elastic Compute Cloud) Systems; how to enforce compliance for infrastructures; and how to deploy code into containers running on ECS (Elastic Container Service).

In this course, learners examine the essential features of AWS (Amazon Web Services) tools and services such as AWS CodePipeline, AWS CodeDeploy, AWS CloudFormation, AWS CodeStar, and AWS CloudWatch to automate important DevOps and CloudOps tasks. This 14-video course recalls features of AWS CodePipeline and how to use it to create automated software release pipelines and deploy sample applications. Discover how to invoke AWS Lambda functions in pipelines using AWS CodePipeline. Set up AWS CodeDeploy to automate deployment of applications. Next, recall the concept of Infrastructure as Code (IaC) and the role of AWS CloudFormation in IaC implementation and management. Understand features of AWS CodeStar to help facilitate developing, building, and deploying applications in AWS. You will learn to manage applications by using CodeStar, source repository, continuous deployment tool-chain, and a project dashboard, and to configure AWS CloudWatch to automate continuous monitoring of resource usage. Finally, discover how to implement continuous security monitoring by using AWS GuardDuty and recall the non-AWS tools that can be used to automate AWS DevOps and AWS CloudOps tasks.

In this course, you will learn objectives of the Topology and Orchestration Specification for Cloud Application (TOSCA) and methodical frameworks that can be used for DevOps artifact transformation with the TOSCA standard. The 14-video course explores DevOps workflow and Azure-specific patterns for automating DevOps workflows, as well as prominent Azure tools and technologies that can be used to implement DevOps and CloudOps practices. Learners will examine different types of version control provided by Azure Repos, along with the key elements and artifacts used in Azure Repos to manage version control and workflows. You will learn how to create and synchronize local Git repositories with centralized Git repositories in Azure DevOps; how to use Azure DevOps to create and manage branches; and how to manage technical debts. Finally, discover how to create Azure CI/CD (continuous integration and continuous deployment) pipelines; how to use Azure Pipelines to implement CI/CD for Node.js applications; and how to automate and execute test cases for web applications.

Learners can explore the useful capabilities of Azure Board for planning, tracking, and monitoring sprints following the Agile methodology, as well as examine the prominent features of Azure Artifacts, and the best practices for managing and sharing packages, in this 16-video course. You will also learn about the configuration management and monitoring capabilities afforded by Azure DevOps along with how to use Azure Pipelines to set up continuous deployment for containerized applications to Azure Kubernetes Service (AKS). Next, you will discover how to create various test artifacts, use Azure DevOps to discover, install, and publish NuGet, NPM (node package manager), and Maven packages, and configure Ansible instances on virtual machines (VMs) by using Ansible Solution Template. Conclude the course by learning how to use Ansible solution template to configure AKS clusters in Azure; how to create resource groups, and create AKS clusters within resource groups; and how to use Azure Blueprint to create, deploy, and update compliant environments.

In this 18-video course, learners explore features of Google Cloud Platform (GCP), Google Kubernetes Engine (GKE), best practices for operating containers, and how to build cloud-native applications with CloudOps methodology. First, compare Google Cloud Source Repository with GitHub, and see how automated deployment capabilities of Google Cloud Deployment Manager compare with Terraform. Then delve into GCP's machine learning (ML), artificial intelligence, and analytical capabilities and essential design patterns for connecting GCP with other cloud platforms. Discover how to create and configure GKE clusters; deploy applications across multiple Kubernetes clusters; create repositories and manage code with Google Cloud Source Repository; and deploy applications from Cloud Source Repository to App Engine. Next, learn how to automate the configuration of GCP resources and application deployment with Google Cloud Deployment Manager, create virtual machine (VM) instances on GCP; build applications with Terraform; and configure AutoML and BigQuery to manage large volumes of data and build ML models. Finally, learners discover how to set up fully-managed real-time messaging environments, and transfer data between GCP and other Cloud Service providers.

Explore the prominent tools and services provided by AWS (Amazon Web Services), Azure, and GCP (Google Cloud Platform) to build, implement, and manage multicloud environments, in this 16-video course. Begin by examining the benefits of adopting the multicloud strategy and multicloud architecture design for application and cloud environment management. Then you will discover the challenges of hybrid and multicloud deployments, how to use the right hybrid and multicloud architecture patterns, the common network topologies for hybrid and multicloud setup, the critical challenges associated with multicloud environments, and the frameworks that can be used to eliminate these challenges. You will learn about the CloudOps characteristics that can be applied to build a cloud management platform for multicloud environments, the tools provided by AWS, Azure, and GCP to implement multicloud environments, the critical patterns for building cloud-specific automations in DevOps pipelines, and the multicloud DevOps framework that provides DevOps solutions for managing multicloud distributed environments.

In this 13-video course, you will learn how to work with tools and services provided by Amazon Web Services (AWS), Azure, and Google Cloud Platform (GCP) to build and implement cross-cloud, multicloud environments. Learners will examine the process of implementing CI/CD (continuous integration/continuous deployment) DevOps pipelines that can employ multicloud environments, along with tools used at each step. Learn how to integrate Azure Active Directory with multiple AWS accounts, use Azure Pipelines to realize the full DevOps cycle with multicloud support, and set up and configure the CloudOps CI/CD process with Spinnaker. Also, explore how to install and configure Jenkins to provision multicloud environments and code; compare tools used to monitor metrics across multicloud environments; and connect GCP with AWS using Virtual Private Networks. Next, learn how to set up and monitor multicloud architecture involving Azure and AWS by using New Relic; view the challenges associated with multicloud disaster recovery (DR) setup; the critical patterns that can be used to set up multicloud DR; and how to set up DR with GCP and AWS.

Final Exam: Cloud Engineer will test your knowledge and application of the topics presented throughout the Cloud Engineer track of the Skillsoft Aspire DevOps Engineer to Cloud Architect Journey.

Explore the differences between DevOps and CloudOps, the challenges and best practices associated with the implementation of CloudOps, and the cloud capabilities that we needs to integrate in existing DevOps implementations to transform DevOps to CloudOps. Discover the prominent multi-cloud management tools that we can use to implement CloudOps, how to select the right multi-cloud management tool, the differences between Hybrid cloud and multi-cloud along with the architectural designs that can lead to effective multi-cloud strategy. We will also discuss how to implement and optimize multi-cloud strategy in the enterprise, the CloudOps design scenarios for managing critical architectural and operational requirement of multi-cloud strategy, how to build DevOps pipelines for multi-cloud application deployment, how to eliminate operational challenges of multi-cloud using the Cloud Service Brokerage platform and mitigate the challenges of designing CloudOps solution for multi-cloud.

"Explore the concept of IaaS, the architecture of IaaS, the key benefits of IaaS with CloudOps; compare IaaS with SaaS, PaaS, and Bare Metal as a Service; the core components of IaaS platform and architecture, when to adopt declarative and imperative IaaS, the prominent IaaS providers, the role of orchestration in implementing CloudOps for heterogeneous deployments, the benefits of cloud orchestration along with the categories of cloud automation and orchestration tools. We will also discuss the multi-cloud strategies and tools for planning and architecting IaaS, the benefits of running containers in multi-cloud environments, the prominent APIs and tools that we can use to run containers in multi-cloud environments, the role of Kubernetes in container management for multi-cloud, the impact of containerization on multi-cloud and edge computing, learn to install and configure OpenStack and OpenNebula on Ubuntu LTS to create multi-cloud models."

In this course, you'll explore the categories of cloud computing portability and interoperability, the interfaces of the Distributed Computing Reference Model, the standardization required for cloud computing portability and interoperability, and the application design principles that can provide better interoperability. You'll learn about the taxonomy of the Cloud Ecosystem Reference Model for designing interoperable cloud architectures, the Enterprise Architecture Principles of the cloud ecosystem that can help maintain consistency and integrity of enterprise architectures, and the prominent cloud deployment models and when to adopt which model. You'll also examine the benefits of achieving cloud interoperability and maximizing portability, the challenges of achieving interoperability between diversified cloud components, the key elements of Interoperability and portability for cloud services, what to consider when defining hybrid cloud strategy, the process-oriented classification of cloud migration, and features exhibited by public, private and hybrid cloud.

In this course, you'll explore scenarios of adopting private and public cloud for application deployment, comparisons of prominent cloud offerings and cloud-based integration tools for deploying and managing varied cloud environments and increasing IT framework complexity, the prominent types of cloud integration, and the benefits of using Integration Platform as a Service. You'll learn about the features of the prominent types of inter-cloud implementations, the recommended architectural patterns for planning multi-cloud migration and deployments, and the multi-cloud implementation workflow for designing automated and portable architectures. You'll also discover how to install, configure, and set up CloudHub and Talend to build portable multi-cloud integration workflows and deploy applications. Finally, you'll examine how to work with Aviatrix to integrate AWS with Azure Active Directory and integrate Azure with AWS and GCP.

In this course, learners will examine the architecture of Docker along with features afforded by Docker to enable and manage multicloud containers, environments, and services. The 12-video course opens with an overview of Docker and the prominent open-source tools that can be used by architects for productive container implementations. You will learn about the role of Docker Container Management in implementing multicloud containerization and the critical Docker services that you need to manage while setting up multicloud containerization. Along the way, explore the features of Docker Cloud and Docker Hub along with the benefits of combining Docker and multicloud computer technologies. You will also learn how to link multiple containers, the steps involved in integrating Docker Cloud with AWS (Amazon Web Services), and how to set up nodes on Docker Cloud and deploy services on the nodes. You will conclude the course by learning how to create multicloud clusters by using Docker Swarm, Docker Machine, and popular cloud platforms.

In this course, you will explore how to work with the features, services, and capabilities provided by Docker Enterprise to enable, implement, and manage federated application and multi-host overlay networking. First, the 12-video course explores the process and the benefits of adopting the federated application pattern. Then, you will learn about the components and features of Docker Enterprise and its role in implementing federated application management architecture. Compare the capabilities of Docker and Kubernetes from the perspective of multicloud orchestration and management; view the Kubernetes federation architecture; and learn to recognize the role of Kubernetes and Docker that follows topology and orchestration-specification for cloud applications. Learn how to implement multi-host overlay network driver to create distributed networks among multiple Docker daemon hosts; connect Docker local client with remote AWS (Amazon Web Services) Docker; and implement multi-host overlay networking by using Etcd. Finally, explore how to create AWS and Azure clusters by using Containership.io and to set up federated application management with Docker Enterprise and Amazon EKS (Elastic Kubernetes Service).

Explore prominent tools that can be used to manage the deployment and life cycle of Kubernetes. In this 15-video course, learn about essential patterns used with Kubernetes to create production level and heterogeneous deployments, as well as how to reduce complexity and risks of multicloud strategy with Kubernetes. Key concepts covered here include the architecture and components of Kubernetes and different types of pods to host vertically-integrated applications; comparing features of Kubernetes and Docker and the solution architectures that can be built; and essential Kubernetes deployment patterns that can be used to deploy application stacks with the Declarative deployment method. Other issues include Fixed and Recreate deployment strategies using Kubernetes; Zero Downtime and No Concurrent Versions deployment, applying the Blue/Green deployment strategy using Kubernetes; and implement Canary testing with Kubernetes Deployments and Services. You will examine Kubernetes Cluster Federation, and how to install and configure multicloud single node Kubernetes clusters on Ubuntu; install and configure multicloud Kubernetes to manage multicloud orchestration; and Extension and Operator patterns used in Kubernetes to improve Kubernetes cluster management capabilities.

In this course, you'll explore the essential components of OpenStack that are used for accessing infrastructure resources. You'll examine topologies of multi-cloud orchestration involving Azure, AWS and OpenStack, as well as the differences between OpenStack and Kubernetes. Next, you'll look at the benefits of using OpenStack and Kubernetes together, the tools that can be used to manage OpenStack deployments, and the critical roles of OpenStack shared services. You'll move on to explore prominent features provided by Heat, the different approaches of deploying OpenStack, and how to get started with the most commonly used OpenStack services. Finally, you'll learn to install OpenStack, create and launch virtual machine instances, and install and configure storage nodes and Heat on OpenStack nodes.

In this course, you'll explore the enterprise and operational factors and SLA considerations that can impact the design of OpenStack cloud. You'll examine open source projects that can be used to implement OpenStack container integration points, prominent containers and Platform as a Service tools that can be deployed on OpenStack Cloud, and the objectives of Magnum. Next, you'll learn about the challenges and associated solutions for implementing OpenStack multi-cloud, how to launch OpenStack instances using Docker Machine, and how to install and configure Docker on OpenStack instances. Finally, you'll discover how to run Kubernetes clusters in the OpenStack ecosystem to deploy application servers, install and configure Jenkins Automation Server, and install OpenStack-Ansible in test environments using general workflows.

Securing multi-cloud deployments and CloudOps platforms involves managing the security and privacy of your cloud services. In this course, you'll explore the key principles and strategies for securing the enterprise cloud, recommended practices and models for perimeter security, and how to choose appropriate security mechanisms. You'll examine the configurable options for security mechanisms on both service and client side, the prominent intrusion detection techniques for managing cloud computing and security challenges, and prominent cloud audit methods. Next, you'll learn approaches for overcoming the impacts of security challenges in multi-cloud environments, best practices for improving the security of multi-cloud deployments, and how to secure CloudOps platforms to build secure pipelines, middleware, and infrastructure for multi-cloud applications. Finally, you'll explore issues a cloud architect should consider when designing cloud solutions and cloud service consumers should evaluate and manage the security and privacy of cloud services.

Azure, AWS, and Google Cloud Platform provide various security services to secure access, applications, data, infrastructures, and deployments in the cloud. In this course, you'll explore the security services provided by AWS Cloud Security for securing workloads and applications, as well as the Azure security products that can be used to protect data, applications, and infrastructures. You'll explore the Google Security Model, the security services provided by Google, why you need to secure containers, and recommended cloud container security best practices for securing container usage in the cloud. You'll move on to learn about how configure AWS Security Hub, create and configure symmetric and asymmetric keys using AWS KMS, and implement Azure Active Directory and Seamless Single Sign-On. Finally, you'll examine how to set up VPN between a Check Point Security Gateway and Cloud VPN on Google Cloud Platform, harden security and restrict network access to the control plane and nodes of Google Kubernetes Engine, and how to encrypt Kubernetes secrets at the application layer.

Implementing the right software-defined wide area network (SD-WAN) framework for the organization can help improve network management, decrease hardware costs, increase efficiency, and even enhance security. In this course, you'll explore the differences between traditional, software-defined networking, and a software-defined wide area network (SD-WAN). You'll learn about the advantages, working mechanisms, solution architecture, and primary components of SD-WAN. Next, you'll examine the topologies and deployment models that can be used to design SD-WAN overlays, along with prominent tools and applications that can be used to build, deploy, and maintain SD-WAN. You'll learn how to apply CloudOps in networks to simplify configuring, managing, and delivering the SD-WAN. You'll examine security challenges and best practices for secure deployment of SD-WAN and prominent Cisco SD-WAN architectures that can be implemented in AWS. You'll see approaches that can be used in an SD-WAN to transform complex legacy networks into easy-to-manage and scalable networks. Finally, you'll learn to configure AWS Transit Gateway, AWS-provided Cisco Cloud Services Router 1000V - BYOL, and VMware SD-WAN.

Final Exam: CloudOps Engineer will test your knowledge and application of the topics presented throughout the CloudOps Engineer track of the Skillsoft Aspire DevOps Engineer to Cloud Architect Journey.

In this course, you'll explore the critical roles and responsibilities of a Cloud Architect, the differences between the roles played by Solution Architects and Cloud Architects, and the cloud challenges that a Cloud Architect needs to manage. You'll learn about the planning solutions Cloud Architects use to automate and orchestrate multi-cloud applications, along with the Cloud Computing Reference Model and Cloud Governance Lifecycle, which are used for building cloud frameworks. Next, you'll look at tools and frameworks that can be used to assess cloud operations and identify the current capabilities of multi-cloud environments, as well as success factors that need to be considered as benchmarks for identifying business values of cloud architectures. Finally, you'll learn about using the Target Operating Model to deliver value through appropriate cloud strategies and using the Multi-cloud Maturity Model and transformation workflows for building solutions for cloud delivery mechanisms.

In this course, you'll explore the role of a cloud architect in transitioning from DevOps to CloudOps. You'll examine the role of a Cloud Operating Model in defining operational processes, as well as the role of the people, organization and operations domain in determining the CloudOps transition process. Next, you'll look at the best practices for transforming enterprise capabilities, the benefits of multi-cloud transition, and the approaches of managing data and information to implement the right cloud transition mechanism. You'll move on to learn about prominent cloud transition strategies for building CloudOps transition roadmaps. You'll explore the systemic principles for planning multi-cloud transitions, tools for planning and deploying CloudOps solutions, and the processes, SLAs, and KPIs associated with running cloud operations that can be used to create operational baseline capabilities. Finally, you'll explore how to facilitate a smooth process, data, and platform transition in multi-cloud environments and the transition path for identifying and applying the right CloudOps Maturity Model.

In this course, you'll learn about the essential components involved in solutioning CloudOps projects, the layered approach of managing enterprise CloudOps applications, and the role-based and layer-based CloudOps solutioning architectures. You'll examine the role of different types of cloud engineers in providing logical and physical architectures for the CloudOps solutioning process. You'll also learn about the role of the Cloud Governance Framework in providing comprehensive cloud-centric governance for each phase of the cloud transformation life cycle used in CloudOps. Next, you'll explore the RACI Matrix of Roles for each CloudOps solutioning stakeholder, the Measures and Metrics involved in solutioning CloudOps strategy, and the anatomy of Cloud Service Agreements. Finally, you'll learn about the logical CloudOps architecture and prominent CloudOps solutioning tools for designing, planning, architecting, and implementing CloudOps solutioning.

In this course, you'll explore the concept of AI Explainability, the role of CloudOps Explainability in managing multi-cloud solutions, how to evaluate explanatory systems, and the properties used to define systems to accommodate explainability approaches. You'll look at how users interact with explainable systems and the effect of explainability on the robustness, security, and privacy aspects of predictive systems. Next, you'll learn about the use of qualitative and quantitative validation approaches and the explainability techniques for defining operational and functional derivatives of cloud operation. You'll examine how to apply explainability throughout the process of operating cloud environments and infrastructures, the methodologies involved in the three stages of AI Explainability in deriving the right CloudOps model for implementation guidance, and the role of explainability in defining AI-assisted Cloud Managed Services. Finally, you'll learn about the architectures that can be derived using Explainable Models, the role of Explainable AI reasoning paths in building trustable CloudOps workflows, and the need for management and governance of AI frameworks in CloudOps architectures.

The impact of future trends allow you to recognize how to build cloud-based applications and architectures that can adopt and adapt to emerging trends, technologies, and processes. In this course, you'll explore future trends in cloud computing and emerging technologies that can help with analyzing their impact on existing cloud operations and practices. You'll learn about solutioning strategies and resource types that can help with adopting emerging technologies and trending features in existing CloudOps architectures. Next, you'll examine cloud application design considerations and the inside-out and outside-in development strategies that can help with adopting and adapting to any futuristic upgrades. You'll learn about how to handle states at the service level, system architecture design considerations, and the use of Adapter pattern to build architectures that adopt and adapt to futuristic innovations. You'll examine the Minimum Valuable Increment design approach, the Manifesto for Future Generation Cloud Computing, and the manifesto areas that are relevant for aligning with the future research directions.

Final Exam: Cloud Architect will test your knowledge and application of the topics presented throughout the Cloud Architect track of the Skillsoft Aspire DevOps Engineer to Cloud Architect Journey.