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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

A hands-on approach with the lab

In this section, you will learn how to build a lambda architecture on the edge using different AWS services. The following diagram shows the lambda architecture:

Figure 5.21 – The lab architecture

The preceding workflow uses the following services. In this chapter, you will complete steps 1–6 (as shown in Figure 5.21). This includes designing and deploying the edge components, processing, and transforming data locally, and pushing the data to different cloud services:

\

Figure 5.22 – The hands-on lab components

In this hands-on section, your objective will consist of the following:

  1. Build the cloud resource (that is, Amazon Kinesis data streams, Amazon S3 bucket, and DynamoDB tables).
  2. Build and deploy the edge components (that is, artifacts and recipes) locally on Raspberry Pi.
  3. Validate that the data is streamed from the edge to the cloud (AWS IoT Core).
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