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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

The anatomy of an edge ML solution

The previous chapter introduced the concept of an edge solution along with the three key kinds of tools that define an edge solution with ML applications. This chapter provides more detail regarding the layers of an edge solution. The three layers addressed in this section are as follows:

  • The business logic layer includes the customized code that dictates the solution's behavior.
  • The physical interface layer connects your solution to the analog world with sensors and actuators.
  • The network interface layer connects your solution to other digital entities in the wider network.

Learning more about these layers is important because they will inform how you, as the IoT architect, make trade-offs when designing your edge ML solution. First, we'll start by defining the business logic layer.

Designing code for business logic

The business logic layer is where all the code of your edge solution lives. This code can take...

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