AI at the Edge
Solving Real-World Problems with Embedded Machine Learning
Will be published approx. on 24. January 2023
Book
Paperback/Softback
512 pages
978-1-0981-2020-7 (ISBN)
Description
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.
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
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.
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
Follow a responsible AI process to create effective products
More details
Language
English
Place of publication
Sebastopol
United States
Product notice
Paperback (trade)
Unsewn / adhesive bound
Dimensions
Height: 231 mm
Width: 178 mm
Thickness: 29 mm
Weight
900 gr
ISBN-13
978-1-0981-2020-7 (9781098120207)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Daniel Situnayake | Jenny Plunkett
AI at the Edge
E-Book
01/2023
O'Reilly
€58.99
Available for download
Daniel Situnayake | Jenny Plunkett
AI at the Edge
E-Book
01/2023
O'Reilly
€58.99
Available for download
Persons
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 co-founded 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. 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. She has presented workshops and tech talks for major tech conferences such as the Grace Hopper Celebration, Edge AI Summit, Embedded Vision Summit, and more. Jenny previously worked as a software engineer and IoT consultant for Arm Mbed and Pelion. She graduated with a B.S. in Electrical Engineering from The University of Texas at Austin.