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Condizione: Good. Item in good condition. Textbooks may not include supplemental items i.e. CDs, access codes etc.

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!

Condizione: New. In.

Condizione: New. In.

Condizione: New. In.

Condizione: New. In.

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 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.

Condizione: New.

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 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.

Condizione: New. In.

Condizione: New.

Condizione: New. 1st edition NO-PA16APR2015-KAP.

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 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.

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Softcover/Paperback. Condizione: Very Good. In zeer goede staat. Paperback. Pocket.699 pp.

Softcover/Paperback. Condizione: New. In zeer goede staat. Ongelezen. Paperback. Pocket.

Couverture souple. Condizione: bon. R150151061: 1980. In-8. Broché. Bon état, Couv. convenable, Dos plié, Intérieur frais. 324 pages. . . . Classification Dewey : 380-Commerce, communications, transports.

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.&Uumlber den Autorrnrn.

Condizione: Hervorragend. Zustand: Hervorragend | Seiten: 308 | Sprache: Englisch | Produktart: Bücher | Wade Hunter's past was marked by violence and homelessness, but he left it all behind when he became a wealthy financier in the East. However, his wealth had also brought him fear and hatred from those who knew him. When Abby Morgan's ranch is in danger of being swindled by a cold-eyed speculator named Dom Wilson, she turns to Wade for help. With the help of notorious assassin Dunigan McLeod, Dom is planning the land swindle of a lifetime. Wade has the cash and connections to save Abby's ranch, but time is running out fast. As he races against the clock, he realizes that there are more subplots at play than he originally thought. He must navigate through a web of deceit and betrayal to uncover the truth behind Dom's plan. As Wade delves deeper into the conspiracy, he discovers that there are more players involved than he originally thought. Every step he takes leads him closer to danger, and the suspense only intensifies with each twist and turn. Amidst the chaos, Wade must also confront his troubled past head-on. His legendary will to fight is put to the test as he faces off against his old enemies. In a thrilling adventure full of suspense and plot twists, Wade fights to save Abby's ranch and bring justice to those who deserve it. In the end, Wade emerges.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

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 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.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Couverture souple. Condizione: bon. RO40212681: 1977. In-8. Broché. Bon état, Couv. convenable, Dos satisfaisant, Intérieur frais. 296 pages. Couverture partiellement décolorée. Scotch jauni au dos des plats. Annotations en page de garde. . . . Classification Dewey : 330-Economie.

Condizione: Used: Acceptable. Occasion - Etat Correct - Jauni, Un peu gondolà - Penser librement (2000) - Grand Format.

Chiron éditeur - 2001 - In-8 broché - 207 pages - nombreuses reproductions photographiques Bon état, étquette de prix sur le bas du quatrième plat.