000			01742nam a22001577a 4500
005			20250313120513.0
020			_a978-1801072168
082			_a006.31 _bPIN-M
100			_aPing, David
245			_aThe Machine Learning Solutions Architect Handbook ; _bCreate machine learning platforms to run solutions in an enterprise setting
260			_aBirmingham UK,. _bPackt Publishing _c2022
300			_a415p.
500			_aWhen equipped with a highly scalable machine learning (ML) platform, organizations can quickly scale the delivery of ML products for faster business value realization. There is a huge demand for skilled ML solutions architects in different industries, and this handbook will help you master the design patterns, architectural considerations, and the latest technology insights you’ll need to become one. You’ll start by understanding ML fundamentals and how ML can be applied to solve real-world business problems. Once you've explored a few leading problem-solving ML algorithms, this book will help you tackle data management and get the most out of ML libraries such as TensorFlow and PyTorch. Using open-source technology such as Kubernetes/Kubeflow to build a data science environment and ML pipelines will be covered next, before moving on to building an enterprise ML architecture using Amazon Web Services (AWS). You’ll also learn about security and governance considerations, advanced ML engineering techniques, and how to apply bias detection, explainability, and privacy in ML model development. By the end of this book, you’ll be able to design and build an ML platform to support common use cases and architecture patterns like a true professional.
504			_aInclude Index.
942			_cBK
999			_c34604 _d34604