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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

Hands-on with the DevOps architecture

In this section, you will learn how to deploy multiple Docker applications to the edge that have already been developed using CI/CD best practices in the cloud. These container images are available in a Docker repository called Docker Hub. The following diagram shows the architecture for this hands-on exercise, where you will complete Steps 1 and 2 to integrate the HBS hub with an existing CI/CD pipeline (managed by your DevOps org), configure the Docker containers, and then deploy and validate them so that they can operate at the edge:

Figure 8.14 – Hands-on DevOps architecture

The following are the services you will use in this exercise:

Figure 8.15 – Services for this exercise

Your objectives for this hands-on section are as follows:

  • Deploy container images from Docker Hub to AWS IoT Greengrass.
  • Confirm that the containers are running.

Let...

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