Applying the Explainability Approach to Guide Cloud Implementation

Skillsoft issued completion badges are earned based on viewing the percentage required or receiving a passing score when assessment is required. 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.