Applying the Explainability Approach to Guide Cloud Implementation

CloudOps
  • 14 Videos | 1h 10m 31s
  • Includes Assessment
  • Earns a Badge
Likes 4 Likes 4
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

  • Playable
    1. 
    Course Overview
    1m 28s
    UP NEXT
  • Playable
    2. 
    AI Explainability and CloudOps
    4m 11s
  • Locked
    3. 
    CloudOps Explainability for Multi-cloud Solutions
    4m 30s
  • Locked
    4. 
    Evaluating Dimensions of Explanatory Systems
    4m 37s
  • Locked
    5. 
    Users and Explainable Systems
    3m 42s
  • Locked
    6. 
    Qualitative and Quantitative Approaches
    6m 5s
  • Locked
    7. 
    Operations and Functional Derivatives
    6m 25s
  • Locked
    8. 
    Explainability and CloudOps Service Delivery
    7m 47s
  • Locked
    9. 
    Stages of AI Explainability
    3m 22s
  • Locked
    10. 
    Role of Explainability for Cloud Managed Services
    5m 39s
  • Locked
    11. 
    Derivable Architectures with Explainable Models
    4m 27s
  • Locked
    12. 
    Explainable AI Reasoning Paths for CloudOps Workflow
    5m 6s
  • Locked
    13. 
    Cloud Explainability for Mitigating Challenges
    5m 1s
  • Locked
    14. 
    Course Summary
    2m 10s

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

Skillsoft is providing you the opportunity to earn a digital badge upon successful completion of this course, which can be shared on any social network or business platform

Digital badges are yours to keep, forever.