AI at the Edge: Solving Real-World Problems with Embedded Machine Learning
Jenny Plunkett · Daniel Situnayake
Nov 2023 · Gildan Media · Narrated by Suzie Althens
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Audiobook
17 hr 17 min
Unabridged
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About this audiobook
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.
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.
About the author
Daniel Situnayake is head of machine learning at Edge Impulse, where he leads embedded machine learning R&D. He is also 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.
Jenny Plunkett is a senior developer relations engineer at Edge Impulse, where she is a technical speaker, developer evangelist, and technical content creator. In addition to maintaining the Edge Impulse documentation, she has also created developer-facing resources for Arm Mbed OS and Pelion IoT.
Suzie Althens records from her professional studio in Alaska, near the beautiful Matanuska Glacier. She narrates regularly for major publishers and specializes in audiobooks and e-learning.
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