000			01250nam a2200193 4500
020			_a9789385889219
082			_a006.31 _bVAL/M
100			_aValliappa Lakshmanan
245			_aMachine Learning Design Patterns _b: solutions to common challenges in data preparation, model building, and MLOps
260			_aMumbai _bShroff Pub _c2021
300			_axiv,390p.
520			_ahe 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			_aMachine Learning
650			_aArtificial Intelligence
650			_aMLOps
700			_aSara Robinson
700			_aMunn, Michael
942			_cBK
999			_c76271 _d76271