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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

Knowledge check

Before moving on to the next chapter, test your knowledge by answering these questions. The answers can be found at the end of the book:

  1. True or false: Two types of ML algorithms exist: supervised and unsupervised.
  2. Can you recall the four types of ML systems and their significance?
  3. True or false: K-means is a classification algorithm.
  4. Can you put the three phases of the ML project life cycle in the right order?
  5. Can you think of at least two common frameworks used for training ML models?
  6. What is the AWS service used for deploying trained models from the cloud to the edge?
  7. True or False: AWS IoT Greengrass only supports custom components for image classification problems.
  8. Can you tell me about one anti-pattern for ML with IoT workloads?
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