What this book covers
Chapter 1, Introduction to the Data-Driven Edge with Machine Learning, introduces concepts such as the edge and how machine learning has a unique value when run at the edge. It provides a high-level overview of use cases in consumer and industrial settings. It sets the context for the fictional scenario that will guide hands-on activities for the book.
Chapter 2, Foundations of Edge Workloads, provides an overview of key considerations for designing edge solutions and includes an introduction to the use of AWS IoT Greengrass.
Chapter 3, Building the Edge, dives into next-level details of building edge solutions through the more advanced use of AWS IoT Greengrass to author software components for your business logic.
Chapter 4, Extending the Cloud to the Edge, introduces how to build edge solutions with native cloud connectivity and deploy software to remote devices over the internet. It also introduces software components provided by AWS for abstracting away common needs for edge functionality.
Chapter 5, Ingesting and Streaming Data from the Edge, introduces how to perform data modeling for IoT workloads and why it's important. It also introduces various architectural patterns and anti-patterns for collecting, ingesting, and processing data streams on the edge.
Chapter 6, Processing and Consuming Data on the Cloud, explains how the integration of IoT with big data technologies enables high-volume complex data processing in the cloud. It also dives deeper into how to extend the data processing design patterns from the edge to the cloud to unblock advanced use cases.
Chapter 7, Machine Learning Workloads at the Edge, introduces the concepts of machine learning in the context of IoT workloads. It also dives deeper into the different phases of machine learning workflow along with applicable design patterns and anti-patterns.
Chapter 8, DevOps and MLOps for the Edge, explains how the concepts of DevOps and MLops can be leveraged for IoT workloads to enable agile development practices from the cloud to the edge.
Chapter 9, Fleet Management at Scale, introduces the concepts of fleet management using cloud-native IoT toolchains. It also dives deeper into the different scenarios and mechanisms applicable for onboarding IoT devices at scale in the real world.
Chapter 10, Reviewing the Solution with AWS Well-Architected Framework, concludes the book with a synopsis of key lessons and steps in terms of how to approach reviewing a solution's design with a multi-faceted review framework from AWS. It also offers ideas on the next steps for IoT architects to take given the lessons learned from this book.
To get the most out of this book
You will need a personal computer running Windows, macOS, or Linux. This computer uses the AWS Command Line Interface in a terminal and the AWS Management Console through a web browser. A second, Linux-based system acts as the edge device and hosts the edge solution running AWS IoT Greengrass. This second system can be a local or remote virtual machine or an actual device like a Raspberry Pi. For the real IoT experience, we recommend using a Raspberry Pi 3B (or later) with a SenseHAT expansion board to complete the hands-on portions of the book. If you do not have a hardware device, you can use an Ubuntu Linux virtual machine instead. Ultimately, you can finish all hands-on steps with or without a second device.
The use of AWS for cloud-based services does incur a small cost. You will need access to an AWS account or create one yourself. Completion of all hands-on sections can accrue billing up to $25 US Dollars (USD). You can opt out of the ML training steps to reduce the cost to less than $5.
At the time of authoring, AWS IoT Greengrass v2 did not support Windows installation. The hands-on portions related to the edge solution are specific to Linux and do not run on Windows.
If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book's GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.
