Heidmann Lynn (11 results)
- Softcover
Seller: World of Books (was SecondSale), Montgomery, IL, U.S.A.World of Books (was SecondSale)
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Condition: Good. Item in good condition. Textbooks may not include supplemental items i.e. CDs, access codes etc.
- Softcover
Seller: HPB-Red, Dallas, TX, U.S.A.HPB-Red
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£ 17.68
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paperback. Condition: 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.
- Softcover
Seller: Rarewaves USA, OSWEGO, IL, U.S.A.Rarewaves USA
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£ 37.97
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Paperback. Condition: 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 provi…de 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
- Softcover
Seller: California Books, Miami, FL, U.S.A.California Books
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£ 42.42
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Condition: New.
- Softcover
Seller: Rarewaves.com USA, London, LONDO, United KingdomRarewaves.com USA
Contact seller5-star sellerCondition: New
£ 45.70
Free ShippingShips from United Kingdom to U.S.A.Quantity: Over 20 available
Paperback. Condition: 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 provi…de 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
- Softcover
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
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£ 42.03
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Condition: New. In.
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
- Softcover
Seller: Majestic Books, Hounslow, United KingdomMajestic Books
Contact seller4-star sellerCondition: New
£ 61.03
£ 6.50 shippingShips from United Kingdom to U.S.A.Quantity: 3 available
Condition: 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
- Softcover
Seller: Books Puddle, New York, NY, U.S.A.Books Puddle
Contact seller4-star sellerCondition: New
£ 69.95
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Condition: New. 1st edition NO-PA16APR2015-KAP.
- Softcover
Seller: Rarewaves USA United, OSWEGO, IL, U.S.A.Rarewaves USA United
Contact seller5-star sellerCondition: New
£ 38.27
£ 37.44 shippingShips within U.S.A.Quantity: Over 20 available
Paperback. Condition: 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 provi…de 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.
Condition: New
£ 50.94
£ 41.96 shippingShips from Germany to U.S.A.Quantity: 1 available
Condition: 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.
- Softcover
Seller: Rarewaves.com UK, London, United KingdomRarewaves.com UK
Contact seller5-star sellerCondition: New
£ 40.90
£ 65.00 shippingShips from United Kingdom to U.S.A.Quantity: Over 20 available
Paperback. Condition: 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 provi…de 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.
