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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

Chapter 9

  1. False – device registration is the process of assigning an identity while device activation is when it comes online for the first time.
  2. A certificate authority can be used with Greengrass to provision certificates for fleets of devices. I can bring my own certificate authority, use one from AWS, or use one from a third-party vendor.
  3. Devices can be provisioned in real time with just-in-time processes such as fleet provisioning and just-in-time provisioning using AWS IoT Core.
  4. False – in addition to metrics and logs, there are further monitoring techniques, such as a heartbeat.
  5. A benefit of using a dashboard to view an entire fleet of devices is to identify outliers and take action on them in a single place, speeding up the time taken for recognition and resolution.
  6. A mitigation strategy for remote troubleshooting is to set up reverse tunneling sessions so that operators can interact with devices over the network without compromising...
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