Intelligent Workloads at the Edge
Deliver cyber-physical outcomes with data and machine learning using AWS IoT Greengrass
Published on 14. January 2022
Book
Paperback/Softback
374 pages
978-1-80181-178-1 (ISBN)
Description
Explore IoT, data analytics, and machine learning to solve cyber-physical problems using the latest capabilities of managed services such as AWS IoT Greengrass and Amazon SageMaker
Key Features
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
Book DescriptionThe 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.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
Who this book is forThis 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.
Key Features
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
Book DescriptionThe 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.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
Who this book is forThis 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.
More details
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
US School Grade: College Graduate Student
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 20 mm
Weight
697 gr
ISBN-13
978-1-80181-178-1 (9781801811781)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Indraneel (Neel) Mitra | Ryan Burke
Intelligent Workloads at the Edge
Deliver cyber-physical outcomes with data and machine learning using AWS IoT Greengrass
E-Book
09/2024
Packt Publishing
from
€34.79
Available for download
Persons
Indraneel Mitra is a Principal Solutions Architect for IoT at Amazon Web Services. He has 17+ years of IT consulting and architecting experience with start ups and Fortune 500 customers across different industry verticals. He has spoken at different events, including AWS re:Invent, AWS Summits, AWS Webinar and Twitch shows, the RSA cybersecurity conference, the Emerging Technologies for Enterprise (ETE) conference, and local meetups on different topics for many years as a seasoned public speaker. Neel is also the co-author and trainer of the popular IoT course, AWS IoT: Developing and Deploying an Internet of Things, available on Coursera and edX. He holds an M.S. in software engineering from BITS Pilani, India.
Ryan Burke is a Senior Sustainability Application Architect at Amazon Web Services, formerly the Worldwide Technical Leader for IoT. He has worked in information technology since 2006, developing web services, user experiences, and architecting IoT solutions. He has spoken to audiences on IoT topics at events including AWS re:Invent, SxSW, TDWI, and local meetup communities. He holds a B.S. in computer science from the Georgia Institute of Technology, and served 6 years as a communications officer in the U.S. Air Force Reserves. In his free time, Ryan enjoys yoga, coffee snobbery, games of all kinds, and annoying his family with a new smart home project.
Ryan Burke is a Senior Sustainability Application Architect at Amazon Web Services, formerly the Worldwide Technical Leader for IoT. He has worked in information technology since 2006, developing web services, user experiences, and architecting IoT solutions. He has spoken to audiences on IoT topics at events including AWS re:Invent, SxSW, TDWI, and local meetup communities. He holds a B.S. in computer science from the Georgia Institute of Technology, and served 6 years as a communications officer in the U.S. Air Force Reserves. In his free time, Ryan enjoys yoga, coffee snobbery, games of all kinds, and annoying his family with a new smart home project.
Content
Table of Contents
Introduction to the Data-Driven Edge with Machine Learning
Foundations of Edge Workloads
Building the Edge
Extending the Cloud to the Edge
Ingesting and Streaming Data from the Edge
Processing and Consuming Data on the Cloud
Machine Learning Workloads at the Edge
DevOps and MLOps for the Edge
Fleet Management at Scale
Reviewing the Solution with AWS Well-Architected Framework
Introduction to the Data-Driven Edge with Machine Learning
Foundations of Edge Workloads
Building the Edge
Extending the Cloud to the Edge
Ingesting and Streaming Data from the Edge
Processing and Consuming Data on the Cloud
Machine Learning Workloads at the Edge
DevOps and MLOps for the Edge
Fleet Management at Scale
Reviewing the Solution with AWS Well-Architected Framework