Book Image

Intelligent Workloads at the Edge

By : Indraneel Mitra, Ryan Burke
Book Image

Intelligent Workloads at the Edge

By: Indraneel 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)
1
Section 1: Introduction and Prerequisites
3
Section 2: Building Blocks
10
Section 3: Scaling It Up
13
Section 4: Bring It All Together

A hands-on approach with the lab

In this section, you will learn how to build a lambda architecture on the edge using different AWS services. The following diagram shows the lambda architecture:

Figure 5.21 – The lab architecture

The preceding workflow uses the following services. In this chapter, you will complete steps 1–6 (as shown in Figure 5.21). This includes designing and deploying the edge components, processing, and transforming data locally, and pushing the data to different cloud services:

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Figure 5.22 – The hands-on lab components

In this hands-on section, your objective will consist of the following:

  1. Build the cloud resource (that is, Amazon Kinesis data streams, Amazon S3 bucket, and DynamoDB tables).
  2. Build and deploy the edge components (that is, artifacts and recipes) locally on Raspberry Pi.
  3. Validate that the data is streamed from the edge to the cloud (AWS IoT Core).
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