000			01684nam a22002177a 4500
005			20260716141722.0
008			260716s2024 |||||||| |||| 00| 0 eng d
020			_a9781492094524
040			_cPK-LaUMT
082			_a006.31 _bSAR-T
100	1		_aSarkis, Anthony _913716
245	1	0	_aTraining data for machine learning : _bhuman supervision from annotation to data science / _cAnthony Sarkis
260			_aBeijing : _bO'Reilly, _c2024
300			_axxii, 306 p.
500			_aIndex present
520			_aYour training data has as much to do with the success of your data project as the algorithms themselves--most failures in deep learning systems relate to training data. But while training data is the foundation for successful machine learning, there are few comprehensive resources to help you ace the process. This hands-on guide explains how to work with and scale training data. Data science professionals and machine learning engineers will gain a solid understanding of the concepts, tools, and processes needed to: Design, deploy, and ship training data for production-grade deep learning applications Integrate with a growing ecosystem of tools Recognize and correct new training data-based failure modes Improve existing system performance and avoid development risks Confidently use automation and acceleration approaches to more effectively create training data Avoid data loss by structuring metadata around created datasets Clearly explain training data concepts to subject matter experts and other shareholders Successfully maintain, operate, and improve your system
546			_aEng
650			_aMachine learning
650			_aData mining
942			_cBK
999			_c141462 _d141462