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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 5: Ingesting and Streaming Data from the Edge

Edge computing can reduce the amount of data transferred to the cloud (or on-premises datacenter), thus saving on network bandwidth costs. Often, high-performance edge applications require local compute, storage, network, data analytics, and machine learning capabilities to process high-fidelity data in low latencies. AWS extends infrastructure to the edge, beyond Regions and Availability Zones, as close to the endpoint as required by your workload. As you will have learned in previous chapters, AWS IoT Greengrass allows you to run sophisticated edge applications on devices and gateways.

In this chapter, you will learn about the different data design and transformation strategies applicable for edge workloads. We will explain how you can ingest data from different sensors through different workflows based on data velocity (such as hot, warm, and cold), data variety (such as structured and unstructured), and data volume (such...

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