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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

Chapter 10: Reviewing the Solution with AWS Well-Architected Framework

You now have the skills required to create edge machine learning (ML) solutions. This chapter acts as both a summary of the key lessons that have been learned throughout this book and follows through on why they are best practices by reviewing the delivered solution. By reviewing the solution, we can see how the Home Base Solutions prototype hub design holds up and where there are further opportunities to improve it. You will learn what it is like to perform a deep analysis of the solution using the AWS Well-Architected Framework, a mechanism that was created for reviewing complex solutions. Finally, we'll leave you with suggested next steps for your journey as a practitioner of delivering intelligent workloads to the edge.

In this chapter, we're going to cover the following main topics:

  • Summarizing the key lessons
  • Describing the AWS Well-Architected Framework
  • Reviewing the solution...
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