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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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1
Section 1: Introduction and Prerequisites
3
Section 2: Building Blocks
10
Section 3: Scaling It Up
13
Section 4: Bring It All Together

Chapter 1

  1. A cyber-physical solution is any interoperation between the analog and digital worlds. An edge solution may include cyber-physical interfaces and also interoperates with other entities in a network topology at a point furthest away from the topology's center of gravity.
  2. Automobiles that are now edge solutions have built-in wireless communications for exchanging messages with a remote service for purposes of logging runtime operations and receiving software updates. For example, Tesla Inc. electric vehicles were designed from day one to be intelligent edge solutions in addition to mechanical vehicles.
  3. No, the telephone was originally a mechanical device. Despite translating analog audio to electrical impulses, this data was not initially processed with computers. Our Chapter 1, Introduction to the Data-Driven Edge with Machine Learning, definition of cyber-physical states that hardware and software are combined to deliver outcomes.
  4. Edge solutions have...
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