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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

The anatomy of an edge ML solution

The previous chapter introduced the concept of an edge solution along with the three key kinds of tools that define an edge solution with ML applications. This chapter provides more detail regarding the layers of an edge solution. The three layers addressed in this section are as follows:

  • The business logic layer includes the customized code that dictates the solution's behavior.
  • The physical interface layer connects your solution to the analog world with sensors and actuators.
  • The network interface layer connects your solution to other digital entities in the wider network.

Learning more about these layers is important because they will inform how you, as the IoT architect, make trade-offs when designing your edge ML solution. First, we'll start by defining the business logic layer.

Designing code for business logic

The business logic layer is where all the code of your edge solution lives. This code can take...

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