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LEADER 00000uam a2200385 a 4500 
003    CaSebORM 
005    20220121231030.0 
006    m     o  d         
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008    210122s2022    xx      o           eng   
024 8  9781098120191 
035    (CaSebORM)9781098120191 
041 0  eng 
100 1  Situnayake, Daniel,|eauthor. 
245 10 AI at the Edge|h[O'Reilly electronic resource] /
       |cSitunayake, Daniel. 
250    1st edition 
264  1 |bO'Reilly Media, Inc.,|c2022. 
300    1 online resource (29 pages) 
336    text|btxt|2rdacontent 
337    computer|bc|2rdamedia 
338    online resource|bcr|2rdacarrier 
347    text file 
365    |b69.99 
520    Edge artificial intelligence is transforming the way 
       computers interact with the real world, allowing internet 
       of things (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 flexible embedded Linux devices-for 
       applications that reduce latency, protect privacy, and 
       work without a network connection, greatly expanding the 
       capabilities of the IoT. This practical guide gives 
       engineering professionals and product managers 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 roadmap will help 
       you get started. Develop your expertise in artificial 
       intelligence and machine learning on edge devices 
       Understand which projects are best solved with edge AI 
       Explore typical design patterns used with edge AI apps Use
       an iterative workflow to develop an edge AI application 
       Optimize models for deployment to embedded devices Improve
       model performance based on feedback from real-world use 
533    Electronic reproduction.|bBoston, MA :|cSafari,|nAvailable
       via World Wide Web. 
538    Mode of access: World Wide Web. 
542    |fCopyright © O'Reilly Media, Inc. 
550    Made available through: Safari, an O'Reilly Media Company.
588 00 Online resource; Title from title page (viewed December 25,
655  7 Electronic books.|2local 
700 1  Plunkett, Jenny,|eauthor. 
710 2  Safari, an O'Reilly Media Company. 
856 40 |zConnect to this resource online|uhttps://