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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

Security at the edge

With IoT security being a hot topic and frequently making headlines, security in your edge ML solutions must be your top priority. Your leadership at HBS will never want to see their company or product name in the news for a story concerning a hacked device. Ultimately, security is about establishing and maintaining trust with your customer. You can use a threat modeling exercise such as STRIDE to analyze atomic actors in your edge system such as end devices, gateways, and software components to reason about worst-case scenarios and the minimum viable mitigation to prevent them. In this section, we will cover common security threats and the best practices for mitigating them.

End devices to your gateway

Let's start with the terminal segment in our edge ML solution along with the appliance monitoring kit itself and its connection to the hub device. The worst-case scenario for this segment is that an unhealthy appliance is mistakenly reported as healthy...

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