AI at the Edge - Situnayake, Daniel; Plunkett, Jenny
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Edge AI is transforming the way computers interact with the real world, allowing IoT devices to make decisions using the 99% of sensor data that was previously discarded due to cost, bandwidth, or power limitations. With techniques like embedded machine learning, developers can capture human intuition and deploy it to any target--from ultra-low power microcontrollers to embedded Linux devices. This practical guide gives engineering professionals, including product managers and technology leaders, an end-to-end framework for solving real-world industrial, commercial, and scientific problems…mehr

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Produktbeschreibung
Edge AI is transforming the way computers interact with the real world, allowing IoT devices to make decisions using the 99% of sensor data that was previously discarded due to cost, bandwidth, or power limitations. With techniques like embedded machine learning, developers can capture human intuition and deploy it to any target--from ultra-low power microcontrollers to embedded Linux devices. This practical guide gives engineering professionals, including product managers and technology leaders, an end-to-end framework for solving real-world industrial, commercial, and scientific problems with edge AI. You'll explore every stage of the process, from data collection to model optimization to tuning and testing, as you learn how to design and support edge AI and embedded ML products. Edge AI is destined to become a standard tool for systems engineers. This high-level road map helps you get started. In this book, you'll develop your expertise in AI and ML for edge devices; understand which projects are best solved with edge AI; explore key design patterns for edge AI apps; learn an iterative workflow for developing AI systems; build a team with the skills to solve real-world problems; and follow a responsible AI process to create effective products.
Autorenporträt
Daniel Situnayake is head of machine learning at Edge Impulse, where he leads embedded machine learning R&D. He's coauthor of the O'Reilly book TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers, the standard textbook on embedded machine learning, and has delivered guest lectures at Harvard, UC Berkeley, and UNIFEI. Dan previously worked on TensorFlow Lite at Google, and cofounded Tiny Farms, the first US company using automation to produce insect protein at industrial scale. He began his career lecturing in automatic identification and data capture at Birmingham City University.