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Intelligent Workloads at the Edge
Intelligent Workloads at the Edge

Intelligent Workloads at the Edge: Deliver cyber-physical outcomes with data and machine learning using AWS IoT Greengrass

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Profile Icon Indraneel (Neel) Mitra Profile Icon Ryan Burke
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€26.98 €29.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8 (17 Ratings)
eBook Jan 2022 374 pages 1st Edition
eBook
€26.98 €29.99
Paperback
€36.99
Subscription
Free Trial
Renews at €18.99p/m
Arrow left icon
Profile Icon Indraneel (Neel) Mitra Profile Icon Ryan Burke
Arrow right icon
€26.98 €29.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8 (17 Ratings)
eBook Jan 2022 374 pages 1st Edition
eBook
€26.98 €29.99
Paperback
€36.99
Subscription
Free Trial
Renews at €18.99p/m
eBook
€26.98 €29.99
Paperback
€36.99
Subscription
Free Trial
Renews at €18.99p/m

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Intelligent Workloads at the Edge

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Key benefits

  • 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 problems

Description

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.

Who is this book for?

This 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.

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 practices

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jan 14, 2022
Length: 374 pages
Edition : 1st
Language : English
ISBN-13 : 9781801818872
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Product Details

Publication date : Jan 14, 2022
Length: 374 pages
Edition : 1st
Language : English
ISBN-13 : 9781801818872
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Concepts :

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Frequently bought together


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Table of Contents

15 Chapters
Section 1: Introduction and Prerequisites Chevron down icon Chevron up icon
Chapter 1: Introduction to the Data-Driven Edge with Machine Learning Chevron down icon Chevron up icon
Section 2: Building Blocks Chevron down icon Chevron up icon
Chapter 2: Foundations of Edge Workloads Chevron down icon Chevron up icon
Chapter 3: Building the Edge Chevron down icon Chevron up icon
Chapter 4: Extending the Cloud to the Edge Chevron down icon Chevron up icon
Chapter 5: Ingesting and Streaming Data from the Edge Chevron down icon Chevron up icon
Chapter 6: Processing and Consuming Data on the Cloud Chevron down icon Chevron up icon
Chapter 7: Machine Learning Workloads at the Edge Chevron down icon Chevron up icon
Section 3: Scaling It Up Chevron down icon Chevron up icon
Chapter 8: DevOps and MLOps for the Edge Chevron down icon Chevron up icon
Chapter 9: Fleet Management at Scale Chevron down icon Chevron up icon
Section 4: Bring It All Together Chevron down icon Chevron up icon
Chapter 10: Reviewing the Solution with AWS Well-Architected Framework Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8
(17 Ratings)
5 star 94.1%
4 star 0%
3 star 0%
2 star 0%
1 star 5.9%
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Harish Jan 24, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I picked this book up as I am working with customers in the Industrial IoT space. The authors have done a great job in explaining the concepts of IoT, data engineering, deploying ML models at the edge. The book does a really good job in breaking down concepts into each chapters. Each chapter in the book starts with an theoretical introduction and dives deeper to explain the concepts and there are also samples code for references.I have borrowed many concepts from the book to help implement production deployment with my customers. This is the only book you will need to implement ML at the edge.
Amazon Verified review Amazon
James Gosling Jan 31, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
For everyone from students, to hardware and software engineers, this book provides remarkably complete and comprehensible coverage for building edge computing devices that interconnect with the cloud, all told in a learn-by-doing style. From simple "hello world" applications to complex image recognition using machine learning. It could even be used as a textbook. One of the special features of this book is that it uses tools from Amazon Web Services to show you how to do remote management. Among other things, whether you're deploying one device, or a million, the management infrastructure scales. Once your IoT device goes from prototype to product, it is secure, updatable and secure.
Amazon Verified review Amazon
Rashed Apr 26, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
A very well written book that is a great self-contained resource to follow and understand. There are real conceptual problems to solve, the information to understand what you're solving, and the steps to perform the solution for yourself. The book doesn't shy away from going deep into complex topics, but manages to present itself in a way that is unintimidating for a reader of any skill—this includes the way the sections and chapters are structured. Great book to keep on hand and use as a reference as well.
Amazon Verified review Amazon
David G Jan 23, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book provides a great introduction to the many aspects of designing and operating workloads for the edge and combined with cloud architectural services; as well as going deeper into design patterns and hands on setup of an edge workload with machine learning.It is easy to read and setup in a step by step format to enable progress through each chapter mixing design and theory with hands on working examples. I recommend this book for those that are interested in designing edge/hybrid workloads, MLOps engineers, and those that are interested in increasing their AWS and IoT knowledge.
Amazon Verified review Amazon
Rob Tiffany Dec 10, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The authors do a great job of plunging you into the AWS world of IoT. From devices to edge computing with AWS IoT Greengrass, and on up the stack to the cloud where machine learning models are trained with Amazon SageMaker, no stone is left unturned. I highly recommend this book to anyone interested in learning how to build IoT solutions.
Amazon Verified review Amazon
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