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Data warehousing in the age of artificial intelligence /

Nearly 7,000 new mobile applications appear every day, and a constant stream of data gives them life. Many organizations rely on a predictive analytics model to turn data into useful business information and ensure the predictions remain accurate as data changes. It can be a complex, time-consuming...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Orenstein, Gary (Autor), Doherty, Conor (Autor), Boyarski, Mike (Autor), Boutin, Eric (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Sebastopol, CA : O'Reilly Media, [2017]
Edición:First edition.
Temas:
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)

MARC

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245 1 0 |a Data warehousing in the age of artificial intelligence /  |c Gary Orenstein, Conor Doherty, Mike Boyarski, and Eric Boutin. 
250 |a First edition. 
264 1 |a Sebastopol, CA :  |b O'Reilly Media,  |c [2017] 
264 4 |c ©2017 
300 |a 1 online resource (1 volume) :  |b illustrations 
336 |a text  |b txt  |2 rdacontent 
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588 0 |a Online resource; title from title page (Safari, viewed January 9, 2019). 
504 |a Includes bibliographical references. 
520 |a Nearly 7,000 new mobile applications appear every day, and a constant stream of data gives them life. Many organizations rely on a predictive analytics model to turn data into useful business information and ensure the predictions remain accurate as data changes. It can be a complex, time-consuming process. This book shows how to automate and accelerate that process using machine learning (ML) on a modern data warehouse that runs on any cloud. Product specialists from MemSQL explain how today's modern data warehouses provide the foundations to implement ML algorithms that run efficiently. Through several real-time use cases, you'll learn how to quickly identify the right metrics to make actionable business decisions. This book explores foundational ML and artificial intelligence concepts to help you understand: How data warehouses accelerate deployment and simplify manageability How companies make a choice between cloud and on-premises deployments for building data processing applications Ways to build analytics and visualizations for business intelligence on historical data The technologies and architecture for building and deploying real-time data pipelines This book demonstrates specific models and examples for building supervised and unsupervised real-time ML applications, and gives practical advice on how to make the choice between building an ML pipeline or buying an existing solution. If you need to use data accurately and efficiently, a real-time data warehouse is a critical business tool. 
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650 0 |a Business enterprises  |x Computer networks  |x Management. 
650 2 |a Artificial Intelligence 
650 6 |a Entrepôts de données (Informatique) 
650 6 |a Intelligence artificielle. 
650 6 |a Réseaux d'ordinateurs  |x Gestion. 
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650 7 |a Information technology  |x Management.  |2 fast  |0 (OCoLC)fst00973112 
700 1 |a Doherty, Conor,  |e author. 
700 1 |a Boyarski, Mike,  |e author. 
700 1 |a Boutin, Eric,  |e author. 
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