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Intelligent Workloads at the Edge

Intelligent Workloads at the Edge

By : Indraneel (Neel) Mitra, Ryan Burke
4.8 (17)
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Intelligent Workloads at the Edge

Intelligent Workloads at the Edge

4.8 (17)
By: Indraneel (Neel) Mitra, Ryan Burke

Overview of this book

The Internet of Things (IoT) has transformed how people think about and interact with the world. The ubiquitous deployment of sensors around us makes it possible to study the world at any level of accuracy and enable data-driven decision-making anywhere. Data analytics and machine learning (ML) powered by elastic cloud computing have accelerated our ability to understand and analyze the huge amount of data generated by IoT. Now, edge computing has brought information technologies closer to the data source to lower latency and reduce costs. This book will teach you how to combine the technologies of edge computing, data analytics, and ML to deliver next-generation cyber-physical outcomes. You’ll begin by discovering how to create software applications that run on edge devices with AWS IoT Greengrass. As you advance, you’ll learn how to process and stream IoT data from the edge to the cloud and use it to train ML models using Amazon SageMaker. The book also shows you how to train these models and run them at the edge for optimized performance, cost savings, and data compliance. By the end of this IoT book, you’ll be able to scope your own IoT workloads, bring the power of ML to the edge, and operate those workloads in a production setting.
Table of Contents (17 chapters)
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Preface
Preface
Who this book is for
What this book covers
Download the example code files
Download the color images
Conventions used
Get in touch
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1
Section 1: Introduction and Prerequisites
Section 1: Introduction and Prerequisites
Free Chapter
2
Chapter 1: Introduction to the Data-Driven Edge with Machine Learning
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Chapter 1: Introduction to the Data-Driven Edge with Machine Learning
Living on the edge
Bringing ML to the edge
Tools to get the job done
Demand for smart home and industrial IoT
Setting the scene: A modern smart home solution
Hands-on prerequisites
Summary
Knowledge check
References
3
Section 2: Building Blocks
Section 2: Building Blocks
4
Chapter 2: Foundations of Edge Workloads
Chapter 2: Foundations of Edge Workloads
Technical requirements
The anatomy of an edge ML solution
IoT Greengrass for the win
Checking compatibility with IoT Device Tester
Installing IoT Greengrass
Creating your first edge component
Summary
Knowledge check
References
5
Chapter 3: Building the Edge
Chapter 3: Building the Edge
Technical requirements
Exploring the topology of the edge
Reviewing common standards and protocols
Security at the edge
Connecting your first device – sensing at the edge
Connecting your second device – actuating at the edge
Summary
Knowledge check
References
6
Chapter 4: Extending the Cloud to the Edge
Chapter 4: Extending the Cloud to the Edge
Technical requirements
Creating and deploying remotely
Storing logs in the cloud
Synchronizing the state between the edge and the cloud
Deploying your first ML model
Summary
Knowledge check
References
7
Chapter 5: Ingesting and Streaming Data from the Edge
Chapter 5: Ingesting and Streaming Data from the Edge
Technical requirements
Defining data models for IoT workloads
Designing data patterns on the edge
A hands-on approach with the lab
Additional topics for reference
Summary
Knowledge check
References
8
Chapter 6: Processing and Consuming Data on the Cloud
Chapter 6: Processing and Consuming Data on the Cloud
Technical requirements
Defining big data for IoT workloads
Designing data patterns on the cloud
A hands-on approach with the lab
Summary
Knowledge check
References
9
Chapter 7: Machine Learning Workloads at the Edge
Chapter 7: Machine Learning Workloads at the Edge
Technical requirements
Defining ML for IoT workloads
Designing an ML workflow in the cloud
Data visualization and analytics
Hands-on with ML architecture
Summary
Knowledge check
References
10
Section 3: Scaling It Up
Section 3: Scaling It Up
11
Chapter 8: DevOps and MLOps for the Edge
Chapter 8: DevOps and MLOps for the Edge
Technical requirements
Defining DevOps for IoT workloads
Understanding the DevOps toolchain for the edge
MLOps at the edge
Hands-on with the DevOps architecture
Summary
Knowledge check
References
12
Chapter 9: Fleet Management at Scale
Chapter 9: Fleet Management at Scale
Technical requirements
Onboarding a fleet of devices globally
Managing your device fleet at scale
Getting hands-on with Fleet Hub architecture
Summary
Knowledge check
References
13
Section 4: Bring It All Together
Section 4: Bring It All Together
14
Chapter 10: Reviewing the Solution with AWS Well-Architected Framework
Chapter 10: Reviewing the Solution with AWS Well-Architected Framework
Summarizing the key lessons
Describing the AWS Well-Architected Framework
Reviewing the solution
Diving deeper into AWS services
Summary
References
15
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Appendix 1 – Answer Key
Appendix 1 – Answer Key
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8
Chapter 9
Why subscribe?

Chapter 1: Introduction to the Data-Driven Edge with Machine Learning

The purpose of this book is to share prescriptive patterns for the end-to-end (E2E) development of solutions that run at the edge, the space in the computing topology nearest to where the analog interfaces the digital and vice versa. Specifically, the book focuses on those edge use cases where machine learning (ML) technologies bring the most value and teaches you how to develop these solutions with contemporary tools provided by Amazon Web Services (AWS).

In this chapter, you will learn about the foundations for cyber-physical outcomes and the challenges, personas, and tools common to delivering these outcomes. This chapter briefly introduces the smart home and industrial internet of things (IoT) settings and sets the scene that will steer the hands-on project built throughout the book. It will describe how ML is transforming our ability to accelerate decision-making beyond the cloud. You will learn about the scope of the E2E project that you will build using AWS services such as AWS IoT Greengrass and Amazon SageMaker. You will also learn what kinds of technical requirements are needed before moving on to the first hands-on chapter, Chapter 2, Foundations of Edge Workloads.

The following topics will be covered in this chapter:

  • Living on the edge
  • Bringing ML to the edge
  • Tools to get the job done
  • Demand for smart home and industrial IoT
  • Setting the scene: A modern smart home solution
  • Hands-on prerequisites
End of Section 1
Next Section
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