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

Knowledge check

Before moving on to the next chapter, test your knowledge by answering these questions. The answers can be found at the end of the book:

  1. True or false: Device registration and device activation are the same.
  2. What are the different ways to leverage a CA with AWS IoT Greengrass?
  3. Is there an option to provision devices in real time? If yes, then what is it?
  4. True or false: Metrics and logs are the only data points required to monitor an IoT workload.
  5. What do you think is a benefit of having a single-pane-of-glass view for your entire fleet of devices?
  6. What is a mitigation strategy for remote troubleshooting devices without sending technicians if required? (Hint: think tunnel.)
  7. What components does AWS IoT Greengrass provide to collect system health telemetry?
  8. True or false: Aggregation of metrics on the edge device is not possible. It can only be done in the cloud.