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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

Defining big data for IoT workloads

The term Big in Big data is relative, as the influx of data has grown substantially in the last two decades from terabytes to exabytes due to the digital transformation of enterprises and connected ecosystems. The advent of big data technologies has allowed people (think social media) and enterprises (think digital transformation) to generate, store, and analyze huge amounts of data. To analyze datasets of this volume, sophisticated computing infrastructure is required that can scale elastically based on the amount of input data and required outcome. This characteristic of big data workloads, along with the availability of cloud computing, democratized the adoption of big data technologies by companies of all sizes. Even with the evolution of edge computing, big data processing on the cloud plays a key role in IoT workloads, as data is more valuable when it's adjacent and enriched with other data systems. In this chapter, we will learn how the...

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