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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

Summarizing the key lessons

In this section, we will group and summarize the key lessons from throughout this book as a quick reference to ensure that the most important lessons were not missed. There is a loose chronology to the groupings based on the material from Chapters 1 to 9, but some lessons may appear in a group outside the order in which they appeared in this book.

Defining edge ML solutions

The following key lessons capture the definition, value proposition, and shape of an edge ML solution:

  • Definition of an edge ML solution: Bringing intelligent workloads to the edge means applying ML technology that's been incorporated into cyber-physical solutions that interoperate the analog and digital spaces. An edge ML solution uses devices that have sufficient compute power to run ML workloads and either directly interface with physical components such as sensors and actuators, or indirectly interface with end devices over a local network or serial protocol.
  • ...
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