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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

Chapter 8

  1. A strategy for developing IoT workloads with more agility is to use DevOps methodology.
  2. False – DevOps is a methodology first from which new tools are designed to support the benefits of faster software delivery with higher quality.
  3. Two challenges for DevOps in IoT workloads are as follows:
    • The edge devices are in remote locations, often outside of our direct control.
    • We cannot throw away edge environments broken by a bad change and start over as we can with a virtual machine in the cloud.
  4. Tools such as AWS IoT Greengrass, CloudFormation, and Terraform are used to design DevOps workflows between the edge and the cloud.
  5. True – running code at the edge in containers and in AWS Lambda functions offers similar benefits (though each has distinctly unique benefits, too).
  6. Three benefits of using MLOps with IoT workloads are productivity (faster iterations
on training and deployment), reliability (using CI/CD practices improves the quality...
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