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paperback. Zustand: 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.

Paperback. Zustand: Very Good. No Jacket. Former library book; May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.

Hardcover. Zustand: Good. Back hinge of spine has a small tear. Cover/edges have minor denting/scuffing.

Zustand: New.

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.…

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Paperback or Softback. Zustand: New. Introducing Mlops: How to Scale Machine Learning in the Enterprise. Book.

Zustand: As New. Unread book in perfect condition.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

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.…

Zustand: New.

Zustand: New.

Zustand: As New. Unread book in perfect condition.

Zustand: New. In English.

Zustand: New.

Zustand: As New. Unread book in perfect condition.

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

Paperback. Zustand: Brand New. 150 pages. 9.50x7.25x0.50 inches. In Stock.

paperback. Zustand: Good. Mark Treveil und das Dataiku-Team: MLOps. Kernkonzepte im Überblick. Machine-Learning-Prozesse im Unternehmen nachhaltig automatisieren und skalieren. Deutsche Ausgabe. Eigenverlag O'Reilly, Heidelberg 2021. Broschur, foliert, Rückenschild, 201 Seiten; Besitz-/Inventarvermerke, sonst ordentlicher Zustand (entwidmetes Bibl.-Ex.). …

Zustand: New.

Zustand: New. 2020. Paperback. . . . . .

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.…

Zustand: New. 2020. Paperback. . . . . . Books ship from the US and Ireland.

Hardcover. Zustand: Brand New. 408 pages. 11.73x8.27x1.10 inches. In Stock.

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.

Zustand: New.

Zustand: New. 206 pages.

Paperback. Zustand: Brand New. German language. 9.37x6.57x0.47 inches. In Stock.

Zustand: New. 206 pages.

paperback. Zustand: New. Language:Chinese.Paperback. Pub Date: 2022-08-01 Pages: 159 Publisher: Machinery Industry Press This book is divided into three parts. Part 1 introduces the topic of MLOps. taking a deep dive into how (and why) it evolved into a discipline. who needs to be involved to successfully execute MLOps. and what components are required. The second part introduces the life cycle of a machine learning model and includes chapters on model development. production preparation. production deployment. monit.…