ISBN: 9781492083290 - introducing mlops: how to scale machine learning in the enterprise (33 Ergebnisse)

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Paperback. Zustand: 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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Paperback. Zustand: 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.…

Paperback. Zustand: new. Paperback. More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Instead, many of these ML models do nothing more than provide static insights in a slideshow. If they aren't truly operational, these models can't possibly do what you've trained them to do.This book introduces practical concepts to help data scientists and application engineers operationalize ML models to drive real business change. Through lessons based on numerous projects around the world, six experts in data analytics provide an applied four-step approach-Build, Manage, Deploy and Integrate, and Monitor-for creating ML-infused applications within your organization.You'll learn how to:Fulfill data science value by reducing friction throughout ML pipelines and workflowsConstantly refine ML models through retraining, periodic tuning, and even complete remodeling to ensure long-term accuracyDesign the ML Ops lifecycle to ensure that people-facing models are unbiased, fair, and explainableOperationalize ML models not only for pipeline deployment but also for external business systems that are more complex and less standardizedPut the four-step Build, Manage, Deploy and Integrate, and Monitor approach into action 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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Paperback. Zustand: 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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Taschenbuch. Zustand: 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 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 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.…

Zustand: 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.

Paperback. Zustand: new. Paperback. More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Instead, many of these ML models do nothing more than provide static insights in a slideshow. If they aren't truly operational, these models can't possibly do what you've trained them to do.This book introduces practical concepts to help data scientists and application engineers operationalize ML models to drive real business change. Through lessons based on numerous projects around the world, six experts in data analytics provide an applied four-step approach-Build, Manage, Deploy and Integrate, and Monitor-for creating ML-infused applications within your organization.You'll learn how to:Fulfill data science value by reducing friction throughout ML pipelines and workflowsConstantly refine ML models through retraining, periodic tuning, and even complete remodeling to ensure long-term accuracyDesign the ML Ops lifecycle to ensure that people-facing models are unbiased, fair, and explainableOperationalize ML models not only for pipeline deployment but also for external business systems that are more complex and less standardizedPut the four-step Build, Manage, Deploy and Integrate, and Monitor approach into action 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…