Lefevre kenji (13 risultati)
- Brossura
Da: HPB-Red, Dallas, TX, U.S.A.HPB-Red
Contatta il venditoreVenditore con 5 stelleCondizione: Usato - Buono
EUR 17,57
EUR 3,21 spedizioneSpedito in U.S.A.Quantità: 1 disponibili
paperback. Condizione: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority.
- Brossura
Da: World of Books (was SecondSale), Montgomery, IL, U.S.A.World of Books (was SecondSale)
Contatta il venditoreVenditore con 5 stelleCondizione: Usato - Buono
EUR 20,92
Spedizione gratuitaSpedito in U.S.A.Quantità: 2 disponibili
Condizione: Good. Item in good condition. Textbooks may not include supplemental items i.e. CDs, access codes etc.
- Brossura
Da: Rarewaves USA, HEBRON, KY, U.S.A.Rarewaves USA
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 43,40
Spedizione gratuitaSpedito in U.S.A.Quantità: Più di 20 disponibili
Paperback. Condizione: 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 prov…ide 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
- Brossura
Da: California Books, Miami, FL, U.S.A.California Books
Contatta il venditoreVenditore con 4 stelleCondizione: Nuovo
EUR 48,49
Spedizione gratuitaSpedito in U.S.A.Quantità: Più di 20 disponibili
Condizione: New.
- Brossura
Da: Rarewaves.com USA, London, LONDO, Regno UnitoRarewaves.com USA
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 52,04
Spedizione gratuitaSpedito da Regno Unito a U.S.A.Quantità: Più di 20 disponibili
Paperback. Condizione: 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 prov…ide 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
- Brossura
Da: Ria Christie Collections, Uxbridge, Regno UnitoRia Christie Collections
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 50,60
EUR 14,00 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 1 disponibili
Condizione: New. In.
Introducing Mlops: How to Scale Machine Learning in the Enterprise
Stenac, Clement/ Dreyfus-schmidt, Leo/ Lefevre, Kenji/ Omont, Nicolas/ Treveil, Mark
- Brossura
Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 62,27
EUR 11,69 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 2 disponibili
Paperback. Condizione: 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
- Brossura
Da: Majestic Books, Hounslow, Regno UnitoMajestic Books
Contatta il venditoreVenditore con 4 stelleCondizione: Nuovo
EUR 69,76
EUR 7,60 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 3 disponibili
Condizione: 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
- Brossura
Da: Books Puddle, New York, NY, U.S.A.Books Puddle
Contatta il venditoreVenditore con 4 stelleCondizione: Nuovo
EUR 80,28
EUR 3,42 spedizioneSpedito in U.S.A.Quantità: 3 disponibili
Condizione: New. 1st edition NO-PA16APR2015-KAP.
- Brossura
Da: Rarewaves USA United, HEBRON, KY, U.S.A.Rarewaves USA United
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 45,63
EUR 42,80 spedizioneSpedito in U.S.A.Quantità: Più di 20 disponibili
Paperback. Condizione: 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 prov…ide 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.
- Brossura
Da: moluna, Greven, Germaniamoluna
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 57,74
EUR 48,99 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Condizione: 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.
- Brossura
Da: Rarewaves.com UK, London, Regno UnitoRarewaves.com UK
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 48,54
EUR 75,97 spedizioneSpedito da Regno Unito a U.S.A.Quantità: Più di 20 disponibili
Paperback. Condizione: 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 prov…ide 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.
- Brossura
Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 66,24
EUR 61,75 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Taschenbuch. Condizione: Neu. Neuware - 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 productio…n 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 workflows- Refine ML models through retraining, periodic tuning, and complete remodeling to ensure long-term accuracy- Design the MLOps life cycle to minimize organizational risks with models that are unbiased, fair, and explainable- Operationalize ML models for pipeline deployment and for external business systems that are more complex and less standardized.
