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

Chapter 7: Machine Learning Workloads at the Edge

The growth of edge computing is not only driven by advancements in computationally efficient hardware devices but also by the advent of different software technologies that were only available on the cloud (or on-premises infrastructure) a decade back. For example, think of smartphones, smartwatches, or personal assistants such as Amazon Alexa that bring a mix of powerful hardware and software capabilities to consumers. Capabilities such as unlocking your phone or garage doors using facial recognition, having a conversation with Alexa using natural language, or riding an autonomous car have become the new normal. Thus, a need for cyber-physical systems to build intelligence throughout their lifetime based on continuous learning from their surroundings has become key for various workloads in today's world.

It's important to realize that most of the top technology companies (such as Apple, Amazon, Google, and Meta, formerly...