Intelligent Workloads at the Edge

Indraneel Mitra, Ryan Burke

• Publisher: Packt Publishing
• Year: 2022
• eBook ISBN: 9781801818872
• Edition: 1
• Topic: Informatik
• Subtopic: Informatik Allgemein

Explore IoT, data analytics, and machine learning to solve cyber-physical problems using the latest capabilities of managed services such as AWS IoT Greengrass and Amazon SageMakerKey Features• Accelerate your next edge-focused product development with the power of AWS IoT Greengrass• Develop proficiency in architecting resilient solutions for the edge with proven best practices• Harness the power of analytics and machine learning for solving cyber-physical problemsBook DescriptionThe 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.What you will learn• Build an end-to-end IoT solution from the edge to the cloud• Design and deploy multi-faceted intelligent solutions on the edge• Process data at the edge through analytics and ML• Package and optimize models for the edge using Amazon SageMaker• Implement MLOps and DevOps for operating an edge-based solution• Onboard and manage fleets of edge devices at scale• Review edge-based workloads against industry best practicesWho this book is forThis book is for IoT architects and software engineers responsible for delivering analytical and machine learning–backed software solutions to the edge. AWS customers who want to learn and build IoT solutions will find this book useful. Intermediate-level experience with running Python software on Linux is required to make the most of this book.

Section 1: Introduction and Prerequisites

This section will introduce you to common edge concepts, personas, challenges, and tools used to deliver edge outcomes. This section will also introduce you to the project that you'll build over the course of the hands-on chapters.

This section comprises the following chapter:

• Chapter 1, Introduction to the Data-Driven Edge with Machine Learning

Chapter 1: Introduction to the Data-Driven Edge with Machine Learning

The purpose of this book is to share prescriptive patterns for the end-to-end (E2E) development of solutions that run at the edge, the space in the computing topology nearest to where the analog interfaces the digital and vice versa. Specifically, the book focuses on those edge use cases where machine learning (ML) technologies bring the most value and teaches you how to develop these solutions with contemporary tools provided by Amazon Web Services (AWS).

In this chapter, you will learn about the foundations for cyber-physical outcomes and the challenges, personas, and tools common to delivering these outcomes. This chapter briefly introduces the smart home and industrial internet of things (IoT) settings and sets the scene that will steer the hands-on project built throughout the book. It will describe how ML is transforming our ability to accelerate decision-making beyond the cloud. You will learn about the scope of the E2E project that you will build using AWS services such as AWS IoT Greengrass and Amazon SageMaker. You will also learn what kinds of technical requirements are needed before moving on to the first hands-on chapter, Chapter 2, Foundations of Edge Workloads.

The following topics will be covered in this chapter:

• Living on the edge
• Bringing ML to the edge
• Tools to get the job done
• Demand for smart home and industrial IoT
• Setting the scene: A modern smart home solution
• Hands-on prerequisites

Living on the edge

The edge is the space of computing topology nearest to where the analog interfaces the digital and vice versa. The edge of the first computing systems, such as 1945's Electronic Numerical Integrator and Computer (ENIAC) general-purpose computer, was simply the interfaces used to input instructions and receive printed output. You couldn't access these interfaces without being directly in front of them. With the advent of remote access mainframe computing in the 1970s, the edge of computing moved further out to public terminals that fit on a desk and connected to mainframes via coaxial cable. Users could access the common resources of the local mainframe from the convenience of a lab or workstation to complete their work with advanced capabilities such as word processors or spreadsheets.

The evolution of humans using the edge for computing continued with increases in compute power and decreases in size and cost. The devices we use every day, such as personal computers and smartphones, deliver myriad outcomes for us. Some outcomes are delivered entirely at the edge (on the device), but many work only when connected to the internet and consume remote services. Edge workloads for humans tend to be diverse, multipurpose, and handle a range of dynamisms. We could not possibly enumerate everything we could do with a smartphone and its web browser! These examples of the edge all have in common that humans are both the operator and recipient of a computing task. However, the edge is more than the interface between humans and silicon.

Another important historical trend of the edge is autonomous functionality. We design computing machines to sense and act, then deploy them in environments where there may be no human interaction at all. Examples of the autonomous edge include robotics used in manufacturing assembly, satellites, and weather stations. These edge workloads are distinct from human-driven workloads in that they tend to be highly specialized, single-purpose, and handle little dynamism. They perform a specific set of functions, perform them consistently, and repeat them until obsolescence. The following figure provides a simplistic history of both human-driven interfaces and autonomous machines at the edge over time:

Figure 1.1 – A timeline of cyber-physical interfaces at the edge from 1950 to 2020

In today's technological advances of wireless communications, microcontrollers and microprocessors, electrical efficiency, and durability, the edge can be anywhere and everywhere. Some of you will be reading this book on an e-reader, a kind of edge device, at 10 km altitude, cruising at 900 kph. The Voyager 1 spacecraft is the most distant manmade edge solution, continuing to operate at the time of this writing 152 AU from Earth! The trend here is that over time, the spectrum of capabilities along the path to and at the edge will continue to grow, as will the length of that path (and the number of points on it!) and the remoteness of where those capabilities can be deployed. The following diagram illustrates the scaling of entities, compute power, and capabilities across the topology of computing:

Figure 1.2 – Illustration of computing scale from the cloud to a sensor

Our world is full of sensors and actuators; over time, more of these devices are joining the IoT. A sensor is any component that takes a measurement from our analog world and converts it to digital data. An actuator is any component that accepts some digital command and applies some force or change out into the analog world. There's so much information out there to collect, reason about, and act upon. Developing edge solutions is an exciting frontier for the following reasons:

• There is a vast set of possibilities and problems to solve in our world today. We need more innovation and solutions to address global outcomes, such as the 17 sustainable development goals defined by the United Nations (UN).
• The shrinking cost factor to develop edge solutions lowers the barrier to experiment.
• Tools that put solution development in the reach of anyone with a desire to learn are maturing and becoming simpler to use.

This book will teach you how to develop the software of edge solutions using modern edge-to-cloud technologies, including how to write software that interacts with physical sensors and actuators, how to process and exchange data with other local devices and the cloud, and how to get value from advanced ML technologies at the edge. More important than the how is the why—in other words: why do we build the solutions this way? This book will also explain the architectural patterns and tips for building well-architected solutions that will last beyond the time of particular technologies and tools.

Implementation details such as programming languages and frameworks come and go with popularity, necessity, and technological breakthroughs. The patterns of what we build and why we build them in particular ways stand the test of time and will serve you for many of your future projects. For example, the 1995 Design Patterns: Elements of Reusable Object-Oriented Software by Gamma, Helm, Johnson, and Vlissides is still guiding software developers today despite the evolution of tools that the authors used at the time. We, the authors, cannot liken ourselves to these great thinkers or their excellent book, but we refer to it as an example of how we approached writing this book.

Common concepts for edge solutions

For the purposes of this book, we will expand the definition of the edge as any component of a cyber-physical solution operating outside of the cloud, its data centers, and away from the internet backbone. Examples of the edge include a radio switch controlling a smart light bulb in a household, sensors recording duty cycles and engine telemetry of a tractor-trailer at a mining site, a turnstile granting access to subway commuters, a weather buoy drifting in the Atlantic, a smartphone using a camera in a new augmented reality (AR) game, and of course, Voyager 1. The environmental control system running in a data center to keep servers cool is still an edge solution; our definition intends to highlight those components that are distant from the gravity of the worldwide computing topology. The following diagram shows examples of computing happening at various distances further from the gravity of data centers in this computing topology:

Figure 1.3 – Examples of the edge at various distances

A cyber-physical solution is one that combines hardware and software for interoperating the digital world with the analog world. If the analog world is a set of p...

Table of contents

• Intelligent Workloads at the Edge
• Contributors
• About the authors
• About the reviewers
• Preface
• Section 1: Introduction and Prerequisites
• Chapter 1: Introduction to the Data-Driven Edge with Machine Learning
• Section 2: Building Blocks
• Chapter 2: Foundations of Edge Workloads
• Chapter 3: Building the Edge
• Chapter 4: Extending the Cloud to the Edge
• Chapter 5: Ingesting and Streaming Data from the Edge
• Chapter 6: Processing and Consuming Data on the Cloud
• Chapter 7: Machine Learning Workloads at the Edge
• Section 3: Scaling It Up
• Chapter 8: DevOps and MLOps for the Edge
• Chapter 9: Fleet Management at Scale
• Section 4: Bring It All Together
• Chapter 10: Reviewing the Solution with AWS Well-Architected Framework
• Appendix 1 – Answer Key
• Other Books You May Enjoy