Intelligent Workloads at the Edge
- Paperback: 374 pages
- Publisher: WOW! eBook (January 14, 2022)
- Language: English
- ISBN-10: 1801811784
- ISBN-13: 978-1801811781
Intelligent Workloads at the Edge: 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
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 Intelligent Workloads at the Edge 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.
- 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
By the end of this Intelligent Workloads at the Edge 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.
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