When transitioning to a CloudOps Engineer role, you're required to recognize where your responsibilities lie and how to embrace proper CloudOps practices. In this course, you'll distinguish the roles of DevOps and CloudOps engineers and explore a CloudOps engineer's role in designing, deploying, and managing end-to-end IT processes and in project management. You'll examine the fundamental factors in setting up a robust CloudOps practice, the primary cloud infrastructure components and technologies, and how to implement DevOps principles to ensure continuous operations in public and private clouds. You'll also investigate the most prominent cloud architectures, automation techniques for composing agile and automated end-to-end IT processes, and software re-architecting and re-engineering approaches. Lastly, you'll identify the significant CloudOps security and compliance challenges you need to mitigate when transitioning from a traditional IT to a CloudOps environment.

To develop into a dependable CloudOps engineer, you need to be conscious of the many hats you must wear and the many situations you need to plan for. In this course, you'll explore the various skills needed by CloudOps engineers, including those required for cloud management, CloudOps component architecting, and to establish collaboration and communication techniques. Next, you'll learn how to identify the need for continuous application optimization in the cloud and outline why and how to adopt cloud reference architecture. You'll also identify optimal CloudOps transformation and modernization methods, how to design a technology upgrade path, and the selection criteria when choosing cloud platforms and tools. Lastly, you'll investigate how to embody a strategic advisor's role and implement appropriate multi-cloud security assessment and defense techniques when designing and architecting CloudOps solutions.

Transitioning to the cloud requires a design-thinking process. In this course, you'll explore the tasks and considerations involved in CloudOps solution design. You'll examine how to shift from a static to a dynamic infrastructure, utilize knowledge exploration and exploitation, and adopt a CloudOps managed strategy and architecture service. You'll investigate the role of prototyping tools, using them to create visual drafts of CloudOps solutions. You'll use Lucidchart with AWS and Azure to present architectural diagrams, visualizations to create CI/CD task boards, and Visual Paradigm to create multi-cloud visual architectures and perform a Five Forces Model analysis. Next, you'll investigate the patterns used to implement reliable and recoverable CloudOps solutions, including health endpoint monitoring patterns. You'll outline the steps involved in managing a hosted cloud service. Lastly, you'll identify the routing requests used in multi-cloud environments.

Continuous automation in CloudOps provides many benefits, but its impact doesn't come without disadvantages. In this course, you'll explore the advantages and disadvantages of several areas of automation, including continuous product delivery, infrastructure provisioning automation, Zero Code multi-cloud automation, and edge networking automation. Next, you'll investigate how to establish an automation governance model and the role of automation and multi-cloud orchestration in moving cloud workloads and managing infrastructure environments. You'll identify prominent multi-cloud management tools and typical use cases of disposable and repeatable infrastructure. Moving on, you'll configure Terraform for automated workflows, use Jenkins and Terraform on Amazon EKS to configure continuous integration, and use Ansible and Terraform to set up environments. Lastly, you'll deploy applications to AWS and configure Cloudify to integrate with an automation toolchain and use a single CI/CD plugin.

Continuous operations and improvements in the cloud is key for successful CloudOps and requires the appropriate components, tools, and best practices to be implemented. Cloud virtualization platforms offer the capability to provide highly redundant, failure-proof systems through the use of hardware and software components. In this course, you'll explore the concept and objective of redundancy and the hierarchical network design used to attempt to eliminate single points of network failure in the network. Part of the discussion includes the key redundancy architectures and factors to consider in providing redundancy in the network design. You'll also examine the features of various cloud computing architectures and differentiate among redundancy management approaches in diversified deployment environments. Next, you'll investigate the need for redundancy in data centers and outline the concept of site redundancy and the role of geo-redundancy in resolving problems of unused computing resources. You'll then identify the need for data replication to achieve redundancy, the features of asynchronous and synchronous replication, along with the conceptual aggregated work path for cloud services to implement redundancy and enable end-to-end availability.

An additional level of redundancy for cloud operations can be achieved by leveraging the resources and architectural components of multiple clouds. In this course you'll explore the concept of the platform as a service (PaaS) framework and the tools available to design diversified architectures. You'll discover open-source and SaaS-based tools used to manage redundant multi-cloud environments, the implementation of geo-redundancy and replication in AWS, Azure, and GCP as well as the key cloud redundancy design principles for building resilient application infrastructures. Also discussed are the key principles and best practices for building multi-cloud deployment platforms and disaster recovery programs. The course also explores recommended design principles and components for cloud data management, architecting successful disaster recovery plans using multi-cloud technologies, and demonstrating how to work with Visio, Arpio, and Hashicorp to design redundant multi-region cloud architectures. Finally, you'll learn how to replicate an AWS environment to an alternate AWS regions and set up redundant environments on AWS and Azure.

Supporting critical CloudOps tasks requires skills and knowledge specific to managing and troubleshooting cloud operations and multi-cloud environments. This course takes a look at essential DevOps elements, the required support architecture, and the automation mindset needed to support critical CloudOps operations. You will explore why creating knowledge bases and establishing key troubleshooting approaches is an essential part of dealing with the challenges and issues of managing multi-cloud environments and storage. Using these tools, you'll be able to identify the root cause of multi-cloud issues and how to troubleshoot the identified issues. In this course, you'll also discover the differences between problem management and incident management, the role of monitoring and alerting systems in resolving operational issues, and the key capabilities of multi-cloud discovery. Finally, the course provides insight into the critical application and business KPIs that can be analyzed to prioritize support tasks.

When designing solutions, CloudOps practitioners need to mitigate typical performance issues. In this course, you'll explore some common performance problems and the systemic tuning approach to improving performance. You'll examine what comprises a performance engineering approach before outlining a practical performance tuning roadmap. Next, you'll identify post-deployment performance diagnostic techniques for large-scale software systems, essential steps when optimizing application performance, and functional and non-functional components and layers to consider when planning performance management. Moving on, you'll outline the steps involved in configuring performance testing and identify critical cloud computing KPIs and metrics. You'll investigate use cases that help identify gaps in hybrid and multi-cloud deployment architectures. You'll examine performance management challenges and recommended solution architecture for cloud-hosted services. Lastly, you'll outline how to measure private and hybrid cloud performance.

Managing the performance of cloud-hosted services doesn't come without challenges. Luckily, there are tools to help you monitor, measure, and improve performance. You'll start this course by exploring common performance management challenges and solutions. You'll then examine parameters to track IT infrastructure and application performance, differentiate between performance and scalability, and identify key metrics to monitor virtualized environments. Next, you'll look in-depth at the purpose of various tools and services, such as the purpose of the five pillars of the AWS framework, how to improve infrastructure resources on Google Cloud Platform, and the role of IBM Cloud Application Performance Management in increasing cloud efficient. You'll also work with CloudWatch Agent, AWS Compute services, Amazon QuickSight, and Application Insight to increase performance efficiency and visibility and troubleshoot issues. Finally, you'll outline how to ensure applications are more scalable, resilient, and manageable in Azure.

Managing the performance of cloud deployments extends to managing multi, hybrid, and multi/hybrid distributed cloud infrastructures. In this course, you'll explore the challenges, goals, and strategies for performance optimization in these environments. You'll start by identifying desired characteristics for successful and robust hybrid or multi-cloud designs. Next, you'll outline how to create a multi-cloud performance optimization strategy and monitor hybrid and multi-cloud distributed infrastructures. You'll then identify common multi-cloud performance challenges and associated solutions. Furthermore, you'll investigate prominent use cases of multi-cloud networking, recommended solutions to resolve multi-cloud network configuration issues, and the different non-functional tests available to determine a multi-cloud performance benchmark. You'll examine significant business and service continuity performance tuning tasks. Lastly, you'll outline how to tune multi-cloud integrator components to resolve connectivity issues between two participating clouds.

CloudOps architects need to explain how their often complex, multi-cloud deployment solutions work to a wide variety of audiences - not an easy feat, but one you'll learn to overcome in this course. You'll start by defining the concept of interpretability and explainability in CloudOps. You'll then outline how to build explainability into a CloudOps workflow, investigating the core explainability principles and benefits along the way. You'll examine the explainability decision tree used to derive a value stream from an existing CloudOps implementation, the algorithms used to explain CloudOps practices, and the governance strategy for deploying explainable cloud applications in multi-cloud environments. You'll examine the challenges and opportunities of explainable AI in CloudOps and identify the key applied intelligence features to derive AIOps solutions. You'll end this course by creating a basic explainability workflow using New Relic.

Final Exam: CloudsOps Engineer will test your knowledge and application of the topics presented throughout the CloudsOps Engineer track of the Skillsoft Aspire Infrastructure Support Engineer to CloudOps Engineer Journey.