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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 5

  1. False – data modeling is applicable for all kinds of databases.
  2. The benefit of performing a data modeling exercise is to select appropriate storage solutions such as a database and a schema.
  3. The relevance of ETL architectures for edge solutions is that we can define multiple data processing paths based on the data's velocity (Lambda architecture). For example, an edge solution can inspect individual sensor measurements to detect alarming peaks while forwarding measurements in bulk to the cloud for cheaper storage.
  4. False – Lambda architecture is a pattern distinct from the Amazon Web Services offering of the same name.
  5. One benefit for data processing at the edge is to perform data cleansing and filtering steps on noisy analog data close to the source before incurring costs of transmitting and storing data on the cloud, where the data may not be used at all.
  6. The minimum component of Greengrass needed to run is the nucleus.
  7. False...
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