Da: Bahamut Media, Reading, Regno Unito
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Aggiungi al carrellopaperback. Condizione: Very Good. Shipped within 24 hours from our UK warehouse. Clean, undamaged book with no damage to pages and minimal wear to the cover. Spine still tight, in very good condition. Remember if you are not happy, you are covered by our 100% money back guarantee.
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Aggiungi al carrelloPaperback. Condizione: New. If you're training a machine learning model but aren't sure how to put it into production, this book will get you there. Kubeflow 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 solvesLearn how to set up Kubeflow on a cloud provider or on an in-house clusterTrain models using Kubeflow with popular tools including scikit-learn, TensorFlow, and Apache SparkLearn how to add custom stages such as serving and predictionKeep your model up-to-date with Kubeflow PipelinesUnderstand how to validate machine learning pipelines.
EUR 47,14
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Aggiungi al carrelloPaperback. Condizione: New. If you're training a machine learning model but aren't sure how to put it into production, this book will get you there. Kubeflow 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 solvesLearn how to set up Kubeflow on a cloud provider or on an in-house clusterTrain models using Kubeflow with popular tools including scikit-learn, TensorFlow, and Apache SparkLearn how to add custom stages such as serving and predictionKeep your model up-to-date with Kubeflow PipelinesUnderstand how to validate machine learning pipelines.
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 37,75
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Aggiungi al carrelloCondizione: New. In.
Lingua: Inglese
Editore: O'Reilly (WILEY UK) 2020-10-20, 2020
ISBN 10: 1492050121 ISBN 13: 9781492050124
Da: Chiron Media, Wallingford, Regno Unito
EUR 35,04
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
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Aggiungi al carrelloPaperback. Condizione: Brand New. 239 pages. 9.00x7.00x0.75 inches. In Stock.
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Aggiungi al carrelloPaperback. Condizione: New. If you're training a machine learning model but aren't sure how to put it into production, this book will get you there. Kubeflow 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 solvesLearn how to set up Kubeflow on a cloud provider or on an in-house clusterTrain models using Kubeflow with popular tools including scikit-learn, TensorFlow, and Apache SparkLearn how to add custom stages such as serving and predictionKeep your model up-to-date with Kubeflow PipelinesUnderstand how to validate machine learning pipelines.
EUR 45,20
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Aggiungi al carrelloKartoniert / Broschiert. Condizione: New. This guide helps data scientists build production-grade machine learning implementations with Kubeflow and shows data engineers how to make models scalable and reliableÜber den AutorrnrnTrevor Grant is a member of the Apache Sof.
EUR 43,58
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Aggiungi al carrelloPaperback. Condizione: New. If you're training a machine learning model but aren't sure how to put it into production, this book will get you there. Kubeflow 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 solvesLearn how to set up Kubeflow on a cloud provider or on an in-house clusterTrain models using Kubeflow with popular tools including scikit-learn, TensorFlow, and Apache SparkLearn how to add custom stages such as serving and predictionKeep your model up-to-date with Kubeflow PipelinesUnderstand how to validate machine learning pipelines.
Da: Revaluation Books, Exeter, Regno Unito
EUR 44,90
Quantità: 1 disponibili
Aggiungi al carrelloPaperback. Condizione: Brand New. 239 pages. 9.00x7.00x0.75 inches. In Stock. This item is printed on demand.