9781098117221 - learning ray: flexible distributed python for machine learning de pumperla, max; oakes, edward; liaw, richard (28 resultados)

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Paperback or Softback. Condición: New. Learning Ray: Flexible Distributed Python for Machine Learning. Book.

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Paperback. Condición: New. Get started with Ray, the open source distributed computing framework that simplifies the process of scaling compute-intensive Python workloads. With this practical book, Python programmers, data engineers, and data scientists will learn how to leverage Ray locally and spin up compute clusters. You'll be able to use Ray to structure and run machine learning programs at scale.Authors Max Pumperla, Edward Oakes, and Richard Liaw show you how to build machine learning applications with Ray. You'll understand how Ray fits into the current landscape of machine learning tools and discover how Ray continues to integrate ever more tightly with these tools. Distributed computation is hard, but by using Ray you'll find it easy to get started.Learn how to build your first distributed applications with Ray CoreConduct hyperparameter optimization with Ray TuneUse the Ray RLlib library for reinforcement learningManage distributed training with the Ray Train libraryUse Ray to perform data processing with Ray DatasetsLearn how work with Ray Clusters and serve models with Ray ServeBuild end-to-end machine learning applications with Ray AIR.…

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PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

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Paperback. Condición: New. Get started with Ray, the open source distributed computing framework that simplifies the process of scaling compute-intensive Python workloads. With this practical book, Python programmers, data engineers, and data scientists will learn how to leverage Ray locally and spin up compute clusters. You'll be able to use Ray to structure and run machine learning programs at scale.Authors Max Pumperla, Edward Oakes, and Richard Liaw show you how to build machine learning applications with Ray. You'll understand how Ray fits into the current landscape of machine learning tools and discover how Ray continues to integrate ever more tightly with these tools. Distributed computation is hard, but by using Ray you'll find it easy to get started.Learn how to build your first distributed applications with Ray CoreConduct hyperparameter optimization with Ray TuneUse the Ray RLlib library for reinforcement learningManage distributed training with the Ray Train libraryUse Ray to perform data processing with Ray DatasetsLearn how work with Ray Clusters and serve models with Ray ServeBuild end-to-end machine learning applications with Ray AIR.…

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Paperback. Condición: new. Paperback. Get started with Ray, the open source distributed computing framework that simplifies the process of scaling compute-intensive Python workloads. With this practical book, Python programmers, data engineers, and data scientists will learn how to leverage Ray locally and spin up compute clusters. You'll be able to use Ray to structure and run machine learning programs at scale. Authors Max Pumperla, Edward Oakes, and Richard Liaw show you how to build machine learning applications with Ray. You'll understand how Ray fits into the current landscape of machine learning tools and discover how Ray continues to integrate ever more tightly with these tools. Distributed computation is hard, but by using Ray you'll find it easy to get started. Learn how to build your first distributed applications with Ray Core Conduct hyperparameter optimization with Ray Tune Use the Ray RLlib library for reinforcement learning Manage distributed training with the Ray Train library Use Ray to perform data processing with Ray Datasets Learn how work with Ray Clusters and serve models with Ray Serve Build end-to-end machine learning applications with Ray AIR With this practical book, Python programmers, data engineers, and data scientists will learn how to leverage Ray locally and spin up compute clusters. You'll be able to use Ray to structure and run machine learning programs at scale. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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Condición: New. 2023. Paperback. . . . . .

Paperback. Condición: New. Get started with Ray, the open source distributed computing framework that simplifies the process of scaling compute-intensive Python workloads. With this practical book, Python programmers, data engineers, and data scientists will learn how to leverage Ray locally and spin up compute clusters. You'll be able to use Ray to structure and run machine learning programs at scale.Authors Max Pumperla, Edward Oakes, and Richard Liaw show you how to build machine learning applications with Ray. You'll understand how Ray fits into the current landscape of machine learning tools and discover how Ray continues to integrate ever more tightly with these tools. Distributed computation is hard, but by using Ray you'll find it easy to get started.Learn how to build your first distributed applications with Ray CoreConduct hyperparameter optimization with Ray TuneUse the Ray RLlib library for reinforcement learningManage distributed training with the Ray Train libraryUse Ray to perform data processing with Ray DatasetsLearn how work with Ray Clusters and serve models with Ray ServeBuild end-to-end machine learning applications with Ray AIR.…

Paperback. Condición: new. Paperback. Get started with Ray, the open source distributed computing framework that simplifies the process of scaling compute-intensive Python workloads. With this practical book, Python programmers, data engineers, and data scientists will learn how to leverage Ray locally and spin up compute clusters. You'll be able to use Ray to structure and run machine learning programs at scale. Authors Max Pumperla, Edward Oakes, and Richard Liaw show you how to build machine learning applications with Ray. You'll understand how Ray fits into the current landscape of machine learning tools and discover how Ray continues to integrate ever more tightly with these tools. Distributed computation is hard, but by using Ray you'll find it easy to get started. Learn how to build your first distributed applications with Ray Core Conduct hyperparameter optimization with Ray Tune Use the Ray RLlib library for reinforcement learning Manage distributed training with the Ray Train library Use Ray to perform data processing with Ray Datasets Learn how work with Ray Clusters and serve models with Ray Serve Build end-to-end machine learning applications with Ray AIR With this practical book, Python programmers, data engineers, and data scientists will learn how to leverage Ray locally and spin up compute clusters. You'll be able to use Ray to structure and run machine learning programs at scale. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

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Taschenbuch. Condición: Neu. Neuware - Get started with Ray, the open source distributed computing framework that simplifies the process of scaling compute-intensive Python workloads. With this practical book, Python programmers, data engineers, and data scientists will learn how to leverage Ray locally and spin up compute clusters. You'll be able to use Ray to structure and run machine learning programs at scale. Authors Max Pumperla, Edward Oakes, and Richard Liaw show you how to build machine learning applications with Ray. You'll understand how Ray fits into the current landscape of machine learning tools and discover how Ray continues to integrate ever more tightly with these tools. Distributed computation is hard, but by using Ray you'll find it easy to get started. - Learn how to build your first distributed applications with Ray Core - Conduct hyperparameter optimization with Ray Tune - Use the Ray RLlib library for reinforcement learning - Manage distributed training with the Ray Train library - Use Ray to perform data processing with Ray Datasets - Learn how work with Ray Clusters and serve models with Ray Serve - Build end-to-end machine learning applications with Ray AIR.…

Condición: New. &Uumlber den AutorMax Pumperla is a data science professor and software engineer located in Hamburg, Germany. He s an active open source contributor, maintainer of several Python packages, and author of machine learning books. He curren.

Paperback. Condición: New. Get started with Ray, the open source distributed computing framework that simplifies the process of scaling compute-intensive Python workloads. With this practical book, Python programmers, data engineers, and data scientists will learn how to leverage Ray locally and spin up compute clusters. You'll be able to use Ray to structure and run machine learning programs at scale.Authors Max Pumperla, Edward Oakes, and Richard Liaw show you how to build machine learning applications with Ray. You'll understand how Ray fits into the current landscape of machine learning tools and discover how Ray continues to integrate ever more tightly with these tools. Distributed computation is hard, but by using Ray you'll find it easy to get started.Learn how to build your first distributed applications with Ray CoreConduct hyperparameter optimization with Ray TuneUse the Ray RLlib library for reinforcement learningManage distributed training with the Ray Train libraryUse Ray to perform data processing with Ray DatasetsLearn how work with Ray Clusters and serve models with Ray ServeBuild end-to-end machine learning applications with Ray AIR.…

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