Librería: California Books, Miami, FL, Estados Unidos de America
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Publicado por Packt Publishing 8/30/2023, 2023
ISBN 10: 1804619280 ISBN 13: 9781804619285
Idioma: Inglés
Librería: BargainBookStores, Grand Rapids, MI, Estados Unidos de America
EUR 48,65
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Añadir al carritoPaperback or Softback. Condición: New. Serverless Machine Learning with Amazon Redshift ML: Create, train, and deploy machine learning models using familiar SQL commands 1.11. Book.
Librería: Books Puddle, New York, NY, Estados Unidos de America
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Publicado por Packt Publishing Limited, 2023
ISBN 10: 1804619280 ISBN 13: 9781804619285
Idioma: Inglés
Librería: PBShop.store UK, Fairford, GLOS, Reino Unido
EUR 52,56
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Añadir al carritoPAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Publicado por Packt Publishing Limited, 2023
ISBN 10: 1804619280 ISBN 13: 9781804619285
Idioma: Inglés
Librería: PBShop.store US, Wood Dale, IL, Estados Unidos de America
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Añadir al carritoPAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 70,84
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Añadir al carritoTaschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Supercharge and deploy Amazon Redshift Serverless, train and deploy machine learning models using Amazon Redshift ML, and run inference queries at scaleKey Features:Leverage supervised learning to build binary classification, multi-class classification, and regression modelsLearn to use unsupervised learning using the K-means clustering methodMaster the art of time series forecasting using Redshift MLPurchase of the print or Kindle book includes a free PDF Elektronisches BuchBook Description:Amazon Redshift Serverless enables organizations to run petabyte-scale cloud data warehouses quickly and in a cost-effective way, enabling data science professionals to efficiently deploy cloud data warehouses and leverage easy-to-use tools to train models and run predictions. This practical guide will help developers and data professionals working with Amazon Redshift data warehouses to put their SQL knowledge to work for training and deploying machine learning models.The book begins by helping you to explore the inner workings of Redshift Serverless as well as the foundations of data analytics and types of data machine learning. With the help of step-by-step explanations of essential concepts and practical examples, you'll then learn to build your own classification and regression models. As you advance, you'll find out how to deploy various types of machine learning projects using familiar SQL code, before delving into Redshift ML. In the concluding chapters, you'll discover best practices for implementing serverless architecture with Redshift.By the end of this book, you'll be able to configure and deploy Amazon Redshift Serverless, train and deploy machine learning models using Amazon Redshift ML, and run inference queries at scale.What You Will Learn:Utilize Redshift Serverless for data ingestion, data analysis, and machine learningCreate supervised and unsupervised models and learn how to supply your own custom parametersDiscover how to use time series forecasting in your data warehouseCreate a SageMaker endpoint and use that to build a Redshift ML model for remote inferenceFind out how to operationalize machine learning in your data warehouseUse model explainability and calculate probabilities with Amazon Redshift MLWho this book is for:Data scientists and machine learning developers working with Amazon Redshift who want to explore its machine-learning capabilities will find this definitive guide helpful. A basic understanding of machine learning techniques and working knowledge of Amazon Redshift is needed to make the most of this book.
Librería: Majestic Books, Hounslow, Reino Unido
EUR 77,74
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Añadir al carritoCondición: New. Print on Demand pp. 384.
Librería: Biblios, Frankfurt am main, HESSE, Alemania
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Añadir al carritoCondición: New. PRINT ON DEMAND pp. 384.