000			01859nam a22003017a 4500
001			22537255
003			IIITD
005			20240504164813.0
008			220506t20202021caua 001 0 eng d
010			_a 2021443780
020			_a9789385889219
040			_aIIITD
082	0	4	_a006.31 _bLAK-M
100	1		_aLakshmanan, Valliappa
245	1	0	_aMachine learning design patterns : _bsolutions to common challenges in data preparation, model building, and MLOps _cby Valliappa Lakshmanan, Sara Robinson and Michael Munn
260			_aBeijng : _bO'Reilly, _c©2022
300			_axiv, 390 p. : _bill. ; _c23 cm.
501			_aIncludes index.
505	0		_tThe need for machine learning design patterns -- Data representation design patterns -- Problem representation design patterns -- Model training patterns -- Design patterns for resilient serving -- Reproducibility design patterns -- Responsible AI -- Connected patterns.
520			_aThe design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice. In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation.--
650		0	_aMachine learning.
650		0	_aBig data.
650		7	_aBig data.
650		7	_aMachine learning.
700	1		_aRobinson, Sara
700	1		_aMunn, Michael
942			_2ddc _cBK
999			_c172548 _d172548