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

  • 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

  • Playable
    1. 
    Course Overview
    1m 26s
    UP NEXT
  • Playable
    2. 
    Interpretability and Explainability
    7m 30s
  • Locked
    3. 
    Design Thinking Process Stages
    7m 46s
  • Locked
    4. 
    Building Explainability into a CloudOps Workflow
    7m 41s
  • Locked
    5. 
    The Challenges and Opportunities of Explainable AI
    6m 45s
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    6. 
    The Explainability Decision Tree
    9m 13s
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    7. 
    The Fundamental Principles of Explainability
    7m 27s
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    8. 
    Algorithms to Explain CloudOps
    6m 2s
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    9. 
    Explainability to Implement Continuous Monitoring
    4m 40s
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    10. 
    Explainability to Achieve Continuous Ops
    7m 38s
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    11. 
    A Governance Strategy and Explainability
    4m 34s
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    12. 
    An AIOps Solution for DevOps
    7m 46s
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    13. 
    Using New Relic to Create an Explainability Workflow
    10m 7s
  • Locked
    14. 
    Course Summary
    1m 26s

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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