Stenac clément (13 resultados)

Introducing MLOps: How to Scale Machine Learning in the Enterprise
Treveil, Mark; Omont, Nicolas; Stenac, Clément; Lefevre, Kenji; Phan, Du; Zentici, Joachim; Lavoillotte, Adrien; Miyazaki, Makoto; Heidmann, Lynn
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Librería: Austin Goodwill 1101, Austin, TX, Estados Unidos de AmericaAustin Goodwill 1101
Contactar con el vendedorVendedor de 5 estrellasCondición: Usado - Aceptable
EUR 11,94
Envío por EUR 2,84Se envía dentro de Estados Unidos de AmericaCantidad disponible: 1 disponibles
Condición: good. Book shows general signs of use and handling. May have light wear on the cover or edges and minimal writing or highlighting. Binding remains tight, and pages are clean and readable.

Introducing MLOps: How to Scale Machine Learning in the Enterprise
Treveil, Mark; Omont, Nicolas; Stenac, Clément; Lefevre, Kenji; Phan, Du; Zentici, Joachim; Lavoillotte, Adrien; Miyazaki, Makoto; Heidmann, Lynn
- Tapa blanda
Librería: Goodwill of Silicon Valley, SAN JOSE, CA, Estados Unidos de AmericaGoodwill of Silicon Valley
Contactar con el vendedorVendedor de 4 estrellasCondición: Usado - Bueno
EUR 15,61
Envío por EUR 3,44Se envía dentro de Estados Unidos de AmericaCantidad disponible: 1 disponibles
Condición: very_good. Supports Goodwill of Silicon Valley job training programs. The cover and pages are in very good condition! The cover and any other included accessories are also in very good condition showing some minor use. The spine is straight, there are no rips tears or creases on the cover or the pages.…

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Librería: Rarewaves USA, HEBRON, KY, Estados Unidos de AmericaRarewaves USA
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 43,67
Gastos de envío gratisSe envía dentro de Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Paperback. Condición: New. More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Some of the challenges and barriers to operationalization are technical, but others are organizational. Either way, the bottom line is that models not in production can't provide business impact.This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time. Through lessons based on numerous MLOps applications around the world, nine experts in machine learning provide insights into the five steps of the model life cycle--Build, Preproduction, Deployment, Monitoring, and Governance--uncovering how robust MLOps processes can be infused throughout.This book helps you:Fulfill data science value by reducing friction throughout ML pipelines and workflowsRefine ML models through retraining, periodic tuning, and complete remodeling to ensure long-term accuracyDesign the MLOps life cycle to minimize organizational risks with models that are unbiased, fair, and explainableOperationalize ML models for pipeline deployment and for external business systems that are more complex and less standardized.…

Introducing MLOps: How to Scale Machine Learning in the Enterprise
Treveil, Mark; Omont, Nicolas; Stenac, Clément; Lefevre, Kenji; Phan, Du; Zentici, Joachim; Lavoillotte, Adrien; Miyazaki, Makoto; Heidmann, Lynn
- Tapa blanda
Librería: California Books, Miami, FL, Estados Unidos de AmericaCalifornia Books
Contactar con el vendedorVendedor de 4 estrellasCondición: Nuevo
EUR 52,34
Gastos de envío gratisSe envía dentro de Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New.

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Librería: Rarewaves.com USA, London, LONDO, Reino UnidoRarewaves.com USA
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 52,36
Gastos de envío gratisSe envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Paperback. Condición: New. More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Some of the challenges and barriers to operationalization are technical, but others are organizational. Either way, the bottom line is that models not in production can't provide business impact.This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time. Through lessons based on numerous MLOps applications around the world, nine experts in machine learning provide insights into the five steps of the model life cycle--Build, Preproduction, Deployment, Monitoring, and Governance--uncovering how robust MLOps processes can be infused throughout.This book helps you:Fulfill data science value by reducing friction throughout ML pipelines and workflowsRefine ML models through retraining, periodic tuning, and complete remodeling to ensure long-term accuracyDesign the MLOps life cycle to minimize organizational risks with models that are unbiased, fair, and explainableOperationalize ML models for pipeline deployment and for external business systems that are more complex and less standardized.…

Introducing Mlops: How to Scale Machine Learning in the Enterprise
Stenac, Clement/ Dreyfus-schmidt, Leo/ Lefevre, Kenji/ Omont, Nicolas/ Treveil, Mark
- Tapa blanda
Librería: Revaluation Books, Exeter, Reino UnidoRevaluation Books
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 62,44
Envío por EUR 11,64Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 2 disponibles
Paperback. Condición: Brand New. 150 pages. 9.50x7.25x0.50 inches. In Stock.

Introducing MLOps: How to Scale Machine Learning in the Enterprise
Treveil, Mark; Omont, Nicolas; Stenac, Clément; Lefevre, Kenji; Phan, Du; Zentici, Joachim; Lavoillotte, Adrien; Miyazaki, Makoto; Heidmann, Lynn
- Tapa blanda
Librería: Majestic Books, Hounslow, Reino UnidoMajestic Books
Contactar con el vendedorVendedor de 4 estrellasCondición: Nuevo
EUR 70,18
Envío por EUR 7,56Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 3 disponibles
Condición: New.

Introducing MLOps: How to Scale Machine Learning in the Enterprise
Treveil, Mark; Omont, Nicolas; Stenac, Clément; Lefevre, Kenji; Phan, Du; Zentici, Joachim; Lavoillotte, Adrien; Miyazaki, Makoto; Heidmann, Lynn
- Tapa blanda
Librería: Ria Christie Collections, Uxbridge, Reino UnidoRia Christie Collections
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 66,64
Envío por EUR 13,14Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 13 disponibles
Condición: New. In English.

Introducing MLOps: How to Scale Machine Learning in the Enterprise
Treveil, Mark; Omont, Nicolas; Stenac, Clément; Lefevre, Kenji; Phan, Du; Zentici, Joachim; Lavoillotte, Adrien; Miyazaki, Makoto; Heidmann, Lynn
- Tapa blanda
Librería: Books Puddle, New York, NY, Estados Unidos de AmericaBooks Puddle
Contactar con el vendedorVendedor de 4 estrellasCondición: Nuevo
EUR 80,78
Envío por EUR 3,44Se envía dentro de Estados Unidos de AmericaCantidad disponible: 3 disponibles
Condición: New. 1st edition NO-PA16APR2015-KAP.

- Tapa blanda
Librería: Rarewaves USA United, HEBRON, KY, Estados Unidos de AmericaRarewaves USA United
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 45,43
Envío por EUR 43,06Se envía dentro de Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Paperback. Condición: New. More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Some of the challenges and barriers to operationalization are technical, but others are organizational. Either way, the bottom line is that models not in production can't provide business impact.This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time. Through lessons based on numerous MLOps applications around the world, nine experts in machine learning provide insights into the five steps of the model life cycle--Build, Preproduction, Deployment, Monitoring, and Governance--uncovering how robust MLOps processes can be infused throughout.This book helps you:Fulfill data science value by reducing friction throughout ML pipelines and workflowsRefine ML models through retraining, periodic tuning, and complete remodeling to ensure long-term accuracyDesign the MLOps life cycle to minimize organizational risks with models that are unbiased, fair, and explainableOperationalize ML models for pipeline deployment and for external business systems that are more complex and less standardized.…

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Librería: moluna, Greven, Alemaniamoluna
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 49,75
Envío por EUR 48,99Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New. This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time.Über den Autorrnrn.

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Librería: Rarewaves.com UK, London, Reino UnidoRarewaves.com UK
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 50,03
Envío por EUR 75,63Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Paperback. Condición: New. More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Some of the challenges and barriers to operationalization are technical, but others are organizational. Either way, the bottom line is that models not in production can't provide business impact.This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time. Through lessons based on numerous MLOps applications around the world, nine experts in machine learning provide insights into the five steps of the model life cycle--Build, Preproduction, Deployment, Monitoring, and Governance--uncovering how robust MLOps processes can be infused throughout.This book helps you:Fulfill data science value by reducing friction throughout ML pipelines and workflowsRefine ML models through retraining, periodic tuning, and complete remodeling to ensure long-term accuracyDesign the MLOps life cycle to minimize organizational risks with models that are unbiased, fair, and explainableOperationalize ML models for pipeline deployment and for external business systems that are more complex and less standardized.…

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- Impresión bajo demanda
Librería: THE SAINT BOOKSTORE, Southport, Reino UnidoTHE SAINT BOOKSTORE
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 54,35
Envío por EUR 14,94Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Paperback / softback. Condición: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.