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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

Managing your device fleet at scale

Although it might be easier to monitor a handful of devices, managing a fleet of devices at scale can turn out to be an operational nightmare. Why? Well, this is because IoT devices (such as the HBS hub) are not just deployed in a controlled perimeter (such as a data center). As you should have gathered by now, these devices can be deployed anywhere, such as home, office, business locations, that might have disparate power utilization, network connectivity, and security postures. For example, there can be times when the devices operate offline and are not available over a public or private network due to the intermittent unavailability of WI-FI connectivity in that premises. Therefore, as an IoT professional, you have to consider various scenarios and plan in advance for managing your fleet at scale.

In the context of a connected HBS hub, device management can help you achieve the following:

  • Capture actionable information from the real...
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