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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

Diving deeper into AWS services

This book focused on a specific use case as a fictitious narrative to selectively highlight features available from AWS that can be used to deliver intelligent workloads to the edge. There is so much more you can achieve with AWS IoT Greengrass, the other services in the AWS IoT suite, the ML suite of services, and the rest of AWS than what we could cover in a single book.

In this section, we will point out a few more features and services that may be of interest to you as an architect in this space, as well as offer some ideas on how to extend the solution you've built so far to further your proficiency.

AWS IoT Greengrass

The Greengrass features we used in the solution represent a subset of the flexibility that Greengrass solutions can offer. You learned how to build with components, deploy software to the edge, fetch ML resources, and make use of built-in features for routing messages throughout the edge and the cloud. The components...

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