Explainability for Cloud Deployments: Applying Explainability in CloudOps
CloudOps 2020
14 Videos | 1h 35m 59s - Includes Assessment
- Earns a Badge
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.
WHAT YOU WILL LEARN
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discover the key concepts covered in this course
define the concept of interpretability and explainability and outline how these can be applied to CloudOps
list the stages of the design thinking process involved in developing an empathic approach to crafting explainability for varying CloudOps users and stakeholders
outline the reasons and methods for building explainability into a CloudOps workflow, with a focus on eliminating the negative impact of IT
state the challenges and opportunities of explainable AI and describe its impact on DevOps principles integrated in CloudOps
describe the explainability decision tree that can be used to derive the value stream of an existing CloudOps implementation
describe the fundamental principles of explainability along with the categories of explanations that help build a CloudOps practice -
recall the different algorithms that can be used to explain CloudOps practices adopted in the enterprise
recognize the benefits of using explainability and specify how it helps configure and implement continuous monitoring and feedback mechanisms
recall the role of explainability in achieving continuous ops in public, private, and multi-cloud environments
describe the governance strategy that needs to be considered when configuring and deploying explainable cloud applications in multi-cloud environments
recognize the features of applied intelligence that help derive an AIOps solution for DevOps, site reliability engineers, and on-call teams to manage CloudOps implementation
create a basic explainability workflow using New Relic by creating policies with their associated conditions
summarize the key concepts covered in this course
IN THIS COURSE
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1.Course Overview1m 26sUP NEXT
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2.Interpretability and Explainability7m 30s
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3.Design Thinking Process Stages7m 46s
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4.Building Explainability into a CloudOps Workflow7m 41s
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5.The Challenges and Opportunities of Explainable AI6m 45s
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6.The Explainability Decision Tree9m 13s
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7.The Fundamental Principles of Explainability7m 27s
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8.Algorithms to Explain CloudOps6m 2s
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9.Explainability to Implement Continuous Monitoring4m 40s
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10.Explainability to Achieve Continuous Ops7m 38s
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11.A Governance Strategy and Explainability4m 34s
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12.An AIOps Solution for DevOps7m 46s
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13.Using New Relic to Create an Explainability Workflow10m 7s
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14.Course Summary1m 26s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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