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9781098103019 - Practical Mlops: Operationalizing Machine Learning Models by Gift, Noah; Deza, Alfredo (36 results)
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Practical MLOps: Operationalizing Machine Learning Models
Language: English
Published by O'Reilly Media (edition 1), 2021
ISBN 10: 1098103017 ISBN 13: 9781098103019
Paperback. Condition: Very Good. 1. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.
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Condition: Like New. paperback. Text block, wrappers and binding are in like new condition, without markings of any kind. Supporting Bay Area Friends of the Library since 2010. Well packaged and promptly shipped.
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Practical Mlops: Operationalizing Machine Learning Models (Paperback or Softback)
Language: English
Published by O'Reilly Media 10/5/2021, 2021
ISBN 10: 1098103017 ISBN 13: 9781098103019
Paperback or Softback. Condition: New. Practical Mlops: Operationalizing Machine Learning Models. Book.
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PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.
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Paperback. Condition: New. Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models.Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging), then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you're trying to crack. This book gives you a head start.You'll discover how to:Apply DevOps best practices to machine learningBuild production machine learning systems and maintain themMonitor, instrument, load-test, and operationalize machine learning systemsChoose the correct MLOps tools for a given machine learning taskRun machine learning models on a variety of platforms and devices, including mobile phones and specialized hardware.
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Practical MLOps
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
Seller rating 4 out of 5 stars
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Add to basketPAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.
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Condition: As New. Unread book in perfect condition.
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Condition: New.
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Practical MLOps (Paperback)
Language: English
Published by O'Reilly Media, Sebastopol, 2021
ISBN 10: 1098103017 ISBN 13: 9781098103019
Paperback. Condition: new. Paperback. Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models.Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging), then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you're trying to crack. This book gives you a head start.You'll discover how to:Apply DevOps best practices to machine learningBuild production machine learning systems and maintain themMonitor, instrument, load-test, and operationalize machine learning systemsChoose the correct MLOps tools for a given machine learning taskRun machine learning models on a variety of platforms and devices, including mobile phones and specialized hardware MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Practical MLOps : Operationalizing Machine Learning Models
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
US$ 65.95
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Practical MLOps
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
Seller rating 5 out of 5 stars
Paperback. Condition: New. Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models.Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging), then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you're trying to crack. This book gives you a head start.You'll discover how to:Apply DevOps best practices to machine learningBuild production machine learning systems and maintain themMonitor, instrument, load-test, and operationalize machine learning systemsChoose the correct MLOps tools for a given machine learning taskRun machine learning models on a variety of platforms and devices, including mobile phones and specialized hardware.
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Practical MLOps: Operationalizing Machine Learning Models
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
US$ 76.81
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Practical MLOps: Operationalizing Machine Learning Models
Language: English
Published by O'Reilly Media 2021-09-30, 2021
ISBN 10: 1098103017 ISBN 13: 9781098103019
US$ 73.13
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Practical MLOps: Operationalizing Machine Learning Models
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Ireland
Seller rating 5 out of 5 stars
Condition: New. 2021. Paperback. . . . . .
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Practical MLOps: Operationalizing Machine Learning Models
Language: English
Published by O'Reilly Media, Inc, USA, 2021
ISBN 10: 1098103017 ISBN 13: 9781098103019
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
Seller rating 5 out of 5 stars
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Practical MLOps : Operationalizing Machine Learning Models
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Practical Mlops: Operationalizing Machine Learning Models
Language: English
Published by Oreilly & Associates Inc, 2021
ISBN 10: 1098103017 ISBN 13: 9781098103019
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Add to basketPaperback. Condition: Brand New. 450 pages. 9.19x7.00x0.93 inches. In Stock.
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Add to basketCondition: NEW.
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Paperback. Condition: New. Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models.Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging), then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you're trying to crack. This book gives you a head start.You'll discover how to:Apply DevOps best practices to machine learningBuild production machine learning systems and maintain themMonitor, instrument, load-test, and operationalize machine learning systemsChoose the correct MLOps tools for a given machine learning taskRun machine learning models on a variety of platforms and devices, including mobile phones and specialized hardware.
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