Applying the Explainability Approach to Guide Cloud Implementation
CloudOps
| Intermediate
- 14 videos | 1h 4m 31s
- Includes Assessment
- Earns a Badge
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.
WHAT YOU WILL LEARN
-
Discover the key concepts covered in this course
Describe the concept of ai explainability and differentiate between ai explainability and cloudops explainability
Describe cloudops explainability and the role it plays in cloudops implementation for managing multi-cloud solutions
Define explanatory systems and evaluate them from functional, operational, usability, security, and validation perspectives
Identify properties that are used to define systems to accommodate explainability approaches and recognize how users interact with explainable systems and what is expected of them
Recall the effect of explainability on the robustness, security, and privacy aspects of predictive systems and describe approaches of evaluating how well the explanation is understood using qualitative and quantitative validation approaches
List explainability techniques that can be used to define operational and functional derivatives of cloudops including leave one column out, permutation impact, and local interpretable model-agnostic explanations -
Recognize the role of explainability and how it can be applied throughout the process of operating cloud environments and infrastructures to ensure efficient service delivery following the cloudops paradigm
Describe the three stages of ai explainability along with the methodologies that are used in each stage to derive the right cloudops model for implementation guidance
Recognize the role of explainability in defining ai-assisted cloud managed services that can be used to manage large cloud enterprise distributed applications
List the architectures that can be derived using explainable models and that can help share cloudops or devops model explainability with the stakeholders to establish better collaboration
Recognize the role of explainable ai reasoning paths in building cloudops workflows that can be trusted by customers, employees, regulators, and other key stakeholders
Describe the role of cloudops and devops explainability in mitigating challenges along with the need for management and governance of ai frameworks in cloudops architectures
Summarize the key concepts covered in this course
IN THIS COURSE
-
1m 28s
-
4m 11sAfter completing this video, you will be able to describe the concept of AI Explainability and differentiate between AI Explainability and CloudOps Explainability. FREE ACCESS
-
3. CloudOps Explainability for Multi-cloud Solutions4m 30sAfter completing this video, you will be able to describe CloudOps Explainability and the role it plays in the implementation of CloudOps for managing multi-cloud solutions. FREE ACCESS
-
4. Evaluating Dimensions of Explanatory Systems4m 37sIn this video, you will learn how to define explanatory systems and how to evaluate them from functional, operational, usability, security, and validation perspectives. FREE ACCESS
-
5. Users and Explainable Systems3m 42sIn this video, you will identify properties that are used to define systems to accommodate explainable approaches and recognize how users interact with explainable systems and what is expected of them. FREE ACCESS
-
6. Qualitative and Quantitative Approaches6m 5sUpon completion of this video, you will be able to recall the effect of explainability on the robustness, security, and privacy aspects of predictive systems and describe approaches of evaluating how well the explanation is understood using qualitative and quantitative validation approaches. FREE ACCESS
-
7. Operations and Functional Derivatives6m 25sAfter completing this video, you will be able to list and explain CloudOps operational and functional derivatives including leave one column out, permutation impact, and local interpretable model-agnostic explanations. FREE ACCESS
-
8. Explainability and CloudOps Service Delivery7m 47sUpon completion of this video, you will be able to recognize the role of explainability and how it can be applied throughout the process of operating cloud environments and infrastructures to ensure efficient service delivery following the CloudOps paradigm. FREE ACCESS
-
9. Stages of AI Explainability3m 22sAfter completing this video, you will be able to describe the three stages of AI Explainability along with the methodologies that are used in each stage to derive the right CloudOps model for implementation guidance. FREE ACCESS
-
10. Role of Explainability for Cloud Managed Services5m 39sAfter completing this video, you will be able to recognize the role of explainability in defining AI-assisted Cloud Managed Services. These services can be used to manage large cloud enterprise distributed applications. FREE ACCESS
-
11. Derivable Architectures with Explainable Models4m 27sUpon completion of this video, you will be able to list the architectures that can be derived using Explainable Models. This will help share CloudOps or DevOps Model Explainability with the stakeholders to establish better collaboration. FREE ACCESS
-
12. Explainable AI Reasoning Paths for CloudOps Workflow5m 6sUpon completion of this video, you will be able to recognize the role of Explainable AI reasoning paths in building CloudOps workflows that can be trusted by customers, employees, regulators, and other key stakeholders. FREE ACCESS
-
13. Cloud Explainability for Mitigating Challenges5m 1sAfter completing this video, you will be able to describe the role of CloudOps and DevOps in mitigating challenges along with the need for management and governance of AI frameworks in CloudOps architectures. FREE ACCESS
-
14. Course Summary2m 10s00f00350-7180-4d9d-8967-2b66d212dbbb FREE ACCESS
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
Skillsoft is providing you the opportunity to earn a digital badge upon successful completion on some of our courses, which can be shared on any social network or business platform.
Digital badges are yours to keep, forever.YOU MIGHT ALSO LIKE
Rating 4.6 of 463 users
(463)
