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 |