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Intelligent Workloads at the Edge

You're reading from   Intelligent Workloads at the Edge Deliver cyber-physical outcomes with data and machine learning using AWS IoT Greengrass

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Product type Paperback
Published in Jan 2022
Publisher Packt
ISBN-13 9781801811781
Length 374 pages
Edition 1st Edition
Tools
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Authors (2):
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Ryan Burke Ryan Burke
Author Profile Icon Ryan Burke
Ryan Burke
Indraneel (Neel) Mitra Indraneel (Neel) Mitra
Author Profile Icon Indraneel (Neel) Mitra
Indraneel (Neel) Mitra
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Toc

Table of Contents (17) Chapters Close

Preface 1. Section 1: Introduction and Prerequisites
2. Chapter 1: Introduction to the Data-Driven Edge with Machine Learning FREE CHAPTER 3. Section 2: Building Blocks
4. Chapter 2: Foundations of Edge Workloads 5. Chapter 3: Building the Edge 6. Chapter 4: Extending the Cloud to the Edge 7. Chapter 5: Ingesting and Streaming Data from the Edge 8. Chapter 6: Processing and Consuming Data on the Cloud 9. Chapter 7: Machine Learning Workloads at the Edge 10. Section 3: Scaling It Up
11. Chapter 8: DevOps and MLOps for the Edge 12. Chapter 9: Fleet Management at Scale 13. Section 4: Bring It All Together
14. Chapter 10: Reviewing the Solution with AWS Well-Architected Framework 15. Other Books You May Enjoy Appendix 1 – Answer Key

Knowledge check

Before moving on to the next chapter, test your knowledge by answering these questions. The answers can be found at the end of the book:

  1. What's the difference between a cyber-physical solution and an edge solution?
  2. At the time it was invented, the automobile was a self-contained mechanical entity, not a cyber-physical solution or an edge solution. At some point in the evolution of the automobile, it started meeting the definition of a cyber-physical solution, and then again meeting the definition of an edge solution. What are the characteristics of automobiles we can find today that meet our definition of an edge solution?
  3. Has the telephone always been a cyber-physical solution? Why or why not?
  4. What are the common components of an edge solution?
  5. What are the three primary types of tools needed to deliver intelligence workloads at the edge?
  6. What are the four key benefits in edge-to-cloud workloads that can be achieved with ML models running at the edge?
  7. Who is the primary persona at the heart of any smart home solution?
  8. Can you identify one more use case for the smart home vertical that ties in with one more of the key benefits for ML-powered edge solutions?
  9. Who is the primary persona at the heart of any industrial solution?
  10. Can you identify one more use case for any industrial vertical that ties in with one more of the key benefits of ML-powered edge solutions?
  11. Is the IoT architect of an ML-powered edge solution typically responsible for the performance accuracy (for example, confidence scores for a prediction) of the models deployed? Why or why not?
You have been reading a chapter from
Intelligent Workloads at the Edge
Published in: Jan 2022
Publisher: Packt
ISBN-13: 9781801811781
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