Intelligent Workloads at the Edge: Deliver cyber-physical outcomes with data and machine learning using AWS IoT Greengrass
Author: Indraneel Mitra and Ryan Burke
Publisher Finelybook 出版社：Packt Publishing (January 14,2022)
pages 页数：374 pages
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
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
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 Author: elastic cloud computing have accelerated our ability to understand and analyze the huge amount of data generated Author: 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 Author: 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.
Author: 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