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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

Understanding the DevOps toolchain for the edge

In the previous chapters, you learned how to develop and deploy native processes, data streams, and ML models on the edge locally and then deployed them at scale using Greengrass's built-in OTA mechanism. We will explain the reverse approach here; that is, building distributed applications on the cloud using DevOps practices and deploying them to the edge. The following diagram shows the approach to continuously build, test, integrate, and deploy workloads using the OTA update mechanism:

Figure 8.7 – A CI/CD view for Edge applications

Figure 8.7 – A CI/CD view for Edge applications

The two most common ways to build a distributed architecture on the edge using AWS IoT Greengrass is by using AWS Lambda services or Docker containers.

AWS Lambda at the edge

I want to make it clear, to avoid any confusion, that the concept of Lambda design, which was introduced in Chapter 5, Ingesting and Streaming Data from the Edge, is an architectural...

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