000			01872nam a22002657a 4500
003			OSt
005			20260602164445.0
008			260602b |||||||| |||| 00| 0 eng d
020			_a9789385889448
041			_aeng
082			_a006.31 GRA-K
100			_aGrant, Trevor _981011
245			_aKubeflow for machine learning: _bfrom lab to production
260			_aMumbai _bShroff Publishers & Distributors Pvt. Ltd. _c2021
300			_axx, 239p.
520			_aKubeflow provides a collection of cloud native tools for different stages of a model's lifecycle, from data exploration, feature preparation, and model training to model serving. This guide helps data scientists build production-grade machine learning implementations with Kubeflow and shows data engineers how to make models scalable and reliable. Using examples throughout the book, authors Holden Karau, Trevor Grant, Ilan Filonenko, Richard Liu, and Boris Lublinsky explain how to use Kubeflow to train and serve your machine learning models on top of Kubernetes in the cloud or in a development environment on-premises. Understand Kubeflow's design, core components, and the problems it solves Understand the differences between Kubeflow on different cluster types Train models using Kubeflow with popular tools including Scikit-learn, TensorFlow, and Apache Spark Keep your model up to date with Kubeflow Pipelines Understand how to capture model training metadata Explore how to extend Kubeflow with additional open source tools Use hyperparameter tuning for training Learn how to serve your model in production
650			_aArtificial Intelligence _981012
650			_aMachine learning _981013
700			_aKarau, Holden _981014
700			_aLublinsky, Boris _981015
700			_aLiu, Richard _981016
700			_aFilonenko, Ilan _981017
942			_cBK
999			_c200025 _d200025