Idioma: Inglés
Publicado por Wiley & Sons, Incorporated, John, 2013
ISBN 10: 1118611934 ISBN 13: 9781118611937
Librería: Better World Books, Mishawaka, IN, Estados Unidos de America
EUR 6,74
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Librería: World of Books (was SecondSale), Montgomery, IL, Estados Unidos de America
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Añadir al carritoSoftcover. Condición: Très bon. Ancien livre de bibliothèque. Légères traces d'usure sur la couverture. Edition 2013. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Very good. Former library book. Slight signs of wear on the cover. Edition 2013. Ammareal gives back up to 15% of this item's net price to charity organizations.
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Añadir al carritopaperback. Condición: Very Good. Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority!
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Añadir al carritoSoftcover. Condición: Fine. Leichte Rillen / Abschürfungen / Risse / Knicke; Gebrochener Buchrücken. The go-to guidebook for deploying Big Data solutions with Hadoop Today's enterprise architects need to understand how the Hadoop frameworks and APIs fit together, and how they can be integrated to deliver real-world solutions.
Librería: Friends of SMPL Bookstore, Santa Monica, CA, Estados Unidos de America
Original o primera edición
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Añadir al carritoPaperback. Condición: 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.
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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
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Librería: Fergies Books, Marietta, GA, Estados Unidos de America
Original o primera edición
EUR 35,42
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Añadir al carritoSoft cover. Condición: New. 1st Edition.
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Añadir al carritoPaperback. Condición: New. Serverless computing enables developers to concentrate solely on their applications rather than worry about where they've been deployed. With the Ray general-purpose serverless implementation in Python, programmers and data scientists can hide servers, implement stateful applications, support direct communication between tasks, and access hardware accelerators.In this book, authors Holden Karau and Boris Lublinsky show you how to scale existing Python applications and pipelines, allowing you to stay in the Python ecosystem while avoiding single points of failure and manual scheduling. If your data processing has grown beyond what a single computer can handle, this book is for you.Written by experienced software architecture practitioners, Scaling Python with Ray is ideal for software architects and developers eager to explore successful case studies and learn more about decision and measurement effectiveness. This book covers distributed processing (the pure Python implementation of serverless) and shows you how to:Implement stateful applications with Ray actorsBuild workflow management in RayUse Ray as a unified platform for batch and streamingImplement advanced data processing with RayApply microservices with Ray platformImplement reliable Ray applications.
EUR 46,23
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Añadir al carritoPaperback. Condición: 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.
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 44,56
Cantidad disponible: 18 disponibles
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EUR 42,73
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Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 36,76
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EUR 54,89
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Añadir al carritoPaperback. Condición: New. Serverless computing enables developers to concentrate solely on their applications rather than worry about where they've been deployed. With the Ray general-purpose serverless implementation in Python, programmers and data scientists can hide servers, implement stateful applications, support direct communication between tasks, and access hardware accelerators.In this book, authors Holden Karau and Boris Lublinsky show you how to scale existing Python applications and pipelines, allowing you to stay in the Python ecosystem while avoiding single points of failure and manual scheduling. If your data processing has grown beyond what a single computer can handle, this book is for you.Written by experienced software architecture practitioners, Scaling Python with Ray is ideal for software architects and developers eager to explore successful case studies and learn more about decision and measurement effectiveness. This book covers distributed processing (the pure Python implementation of serverless) and shows you how to:Implement stateful applications with Ray actorsBuild workflow management in RayUse Ray as a unified platform for batch and streamingImplement advanced data processing with RayApply microservices with Ray platformImplement reliable Ray applications.
Librería: SHIMEDIA, Brooklyn, NY, Estados Unidos de America
EUR 57,62
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Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 41,80
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Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 42,72
Cantidad disponible: 18 disponibles
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Idioma: Inglés
Publicado por Oreilly & Associates Inc, 2020
ISBN 10: 1492050121 ISBN 13: 9781492050124
Librería: Revaluation Books, Exeter, Reino Unido
EUR 49,46
Cantidad disponible: 2 disponibles
Añadir al carritoPaperback. Condición: Brand New. 239 pages. 9.00x7.00x0.75 inches. In Stock.
Librería: Ria Christie Collections, Uxbridge, Reino Unido
EUR 49,99
Cantidad disponible: 3 disponibles
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