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Intelligent Workloads at the Edge

Intelligent Workloads at the Edge

By : Indraneel (Neel) Mitra, Ryan Burke
4.8 (17)
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Intelligent Workloads at the Edge

Intelligent Workloads at the Edge

4.8 (17)
By: Indraneel (Neel) 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)
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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. Can you think of at least two benefits of domain-driven design from the standpoint of edge workloads?
  2. True or false: bounded context and ubiquitous language are the same.
  3. What do you think is necessary to have an operational datastore or a data lake/data warehouse?
  4. Can you recall the design pattern name that brings together streaming and batch workflows?
  5. What strategy could you incorporate to transform raw data on the cloud?
  6. True or false: You cannot access data from a NoSQL data store through APIs.
  7. When would you use a mediator versus broker topology for the event-driven workload?
  8. Can you think of at least one benefit of using a serverless function for processing IoT data?
  9. What business intelligence (BI) services can you use for data exposition to end consumers?
  10. True or false: JSON is the...
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