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.

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Spis treści

Intelligent Workloads at the Edge. Deliver cyber-physical outcomes with data and machine learning using AWS IoT Greengrass eBook -- spis treści

• 1. Introduction to the Data-Driven Edge with Machine Learning
• 2. Foundations of Edge Workloads
• 3. Building the Edge
• 4. Extending the Cloud to the Edge
• 5. Ingesting and Streaming Data from the Edge
• 6. Processing and Consuming Data on the Cloud
• 7. Machine Learning Workloads at the Edge
• 8. DevOps and MLOps for the Edge
• 9. Fleet Management at Scale
• 10. Reviewing the Solution with AWS Well-Architected Framework