Machine Learning Production Systems by Crowe Robert (25 results)
- Softcover
Seller: Goodwill of Silicon Valley, SAN JOSE, CA, U.S.A.Goodwill of Silicon Valley
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Condition: good. Supports Goodwill of Silicon Valley job training programs. The cover and pages are in Good condition! Any other included accessories are also in Good condition showing use. Use can include some highlighting and writing, page and cover creases as well as other types visible wear.
Machine Learning Production Systems : Engineering Machine Learning Models and Pipelines
Crowe, Robert; Hapke, Hannes; Caveness, Emily; Zhu, Di; Nelson, Catherine
- Softcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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Published by O'Reilly Media
- Softcover
Seller: Academic Book Solutions, Medford, NY, U.S.A.Academic Book Solutions
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Add to basketpaperback. Condition: VeryGood. A copy that may have been read, very minimal wear and tear. May have a remainder mark.
- Softcover
Seller: BargainBookStores, Grand Rapids, MI, U.S.A.BargainBookStores
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Paperback or Softback. Condition: New. Machine Learning Production Systems: Engineering Machine Learning Models and Pipelines. Book.
- Softcover
Seller: PBShop.store US, Wood Dale, IL, U.S.A.PBShop.store US
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PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.
Machine Learning Production Systems : Engineering Machine Learning Models and Pipelines
Crowe, Robert; Hapke, Hannes; Caveness, Emily; Zhu, Di; Nelson, Catherine
- Softcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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- Softcover
Seller: Rarewaves USA, OSWEGO, IL, U.S.A.Rarewaves USA
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Paperback. Condition: New. Using machine learning for products, services, and critical business processes is quite different from using ML in an academic or research setting-especially for recent ML graduates and those moving from research to a commercial environment. Whether you currently work to create products and services th…at use ML, or would like to in the future, this practical book gives you a broad view of the entire field.Authors Robert Crowe, Hannes Hapke, Emily Caveness, and Di Zhu help you identify topics that you can dive into deeper, along with reference materials and tutorials that teach you the details. You'll learn the state of the art of machine learning engineering, including a wide range of topics such as modeling, deployment, and MLOps. You'll learn the basics and advanced aspects to understand the production ML lifecycle.This book provides four in-depth sections that cover all aspects of machine learning engineering:Data: collecting, labeling, validating, automation, and data preprocessing; data feature engineering and selection; data journey and storageModeling: high performance modeling; model resource management techniques; model analysis and interoperability; neural architecture searchDeployment: model serving patterns and infrastructure for ML models and LLMs; management and delivery; monitoring and loggingProductionalizing: ML pipelines; classifying unstructured texts and images; genAI model pipelines.
- Softcover
Seller: California Books, Miami, FL, U.S.A.California Books
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- Softcover
Seller: PBShop.store UK, Fairford, GLOS, United KingdomPBShop.store UK
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PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.
- Softcover
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
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Paperback. Condition: new. Paperback. Using machine learning for products, services, and critical business processes is quite different from using ML in an academic or research setting--especially for recent ML graduates and those moving from research to a commercial environment. Whether you currently work to create products and… services that use ML, or would like to in the future, this practical book gives you a broad view of the entire field. Authors Robert Crowe, Hannes Hapke, Emily Caveness, and Di Zhu help you identify topics that you can dive into deeper, along with reference materials and tutorials that teach you the details. You'll learn the state of the art of machine learning engineering, including a wide range of topics such as modeling, deployment, and MLOps. You'll learn the basics and advanced aspects to understand the production ML lifecycle. This book provides four in-depth sections that cover all aspects of machine learning engineering: Data: collecting, labeling, validating, automation, and data preprocessing; data feature engineering and selection; data journey and storage Modeling: high performance modeling; model resource management techniques; model analysis and interoperability; neural architecture search Deployment: model serving patterns and infrastructure for ML models and LLMs; management and delivery; monitoring and logging Productionalizing: ML pipelines; classifying unstructured texts and images; genAI model pipelines Whether you currently work to create products and services that use machine learning, or would like to in the future, this practical book teaches you the basics and advanced aspects of the production ML lifecycle. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Machine Learning Production Systems : Engineering Machine Learning Models and Pipelines
Crowe, Robert; Hapke, Hannes; Caveness, Emily; Zhu, Di; Nelson, Catherine
- Softcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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- Softcover
Seller: Rarewaves.com USA, London, LONDO, United KingdomRarewaves.com USA
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Paperback. Condition: New. Using machine learning for products, services, and critical business processes is quite different from using ML in an academic or research setting-especially for recent ML graduates and those moving from research to a commercial environment. Whether you currently work to create products and services th…at use ML, or would like to in the future, this practical book gives you a broad view of the entire field.Authors Robert Crowe, Hannes Hapke, Emily Caveness, and Di Zhu help you identify topics that you can dive into deeper, along with reference materials and tutorials that teach you the details. You'll learn the state of the art of machine learning engineering, including a wide range of topics such as modeling, deployment, and MLOps. You'll learn the basics and advanced aspects to understand the production ML lifecycle.This book provides four in-depth sections that cover all aspects of machine learning engineering:Data: collecting, labeling, validating, automation, and data preprocessing; data feature engineering and selection; data journey and storageModeling: high performance modeling; model resource management techniques; model analysis and interoperability; neural architecture searchDeployment: model serving patterns and infrastructure for ML models and LLMs; management and delivery; monitoring and loggingProductionalizing: ML pipelines; classifying unstructured texts and images; genAI model pipelines.
- Softcover
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
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Condition: New. In.
- Softcover
Seller: Majestic Books, Hounslow, United KingdomMajestic Books
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Condition: New.
Machine Learning Production Systems : Engineering Machine Learning Models and Pipelines
Crowe, Robert; Hapke, Hannes; Caveness, Emily; Zhu, Di; Nelson, Catherine
- Softcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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£ 50.48
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Condition: As New. Unread book in perfect condition.
- Softcover
Seller: Brook Bookstore On Demand, Napoli, NA, ItalyBrook Bookstore On Demand
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- Softcover
Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios
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Machine Learning Production Systems: Engineering Machine Learning Models and Pipelines
Crowe, Robert/ Hapke, Hannes/ Caveness, Emily/ Zhu, Di/ Nelson, Catherine
- Softcover
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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£ 67.11
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Paperback. Condition: Brand New. 2nd edition. 260 pages. 9.19x7.00x9.19 inches. In Stock.
- Softcover
Seller: Speedyhen, Hertfordshire, United KingdomSpeedyhen
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Condition: NEW.
- Softcover
Seller: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
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£ 64.11
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Paperback. Condition: new. Paperback. Using machine learning for products, services, and critical business processes is quite different from using ML in an academic or research setting--especially for recent ML graduates and those moving from research to a commercial environment. Whether you currently work to create products and… services that use ML, or would like to in the future, this practical book gives you a broad view of the entire field. Authors Robert Crowe, Hannes Hapke, Emily Caveness, and Di Zhu help you identify topics that you can dive into deeper, along with reference materials and tutorials that teach you the details. You'll learn the state of the art of machine learning engineering, including a wide range of topics such as modeling, deployment, and MLOps. You'll learn the basics and advanced aspects to understand the production ML lifecycle. This book provides four in-depth sections that cover all aspects of machine learning engineering: Data: collecting, labeling, validating, automation, and data preprocessing; data feature engineering and selection; data journey and storage Modeling: high performance modeling; model resource management techniques; model analysis and interoperability; neural architecture search Deployment: model serving patterns and infrastructure for ML models and LLMs; management and delivery; monitoring and logging Productionalizing: ML pipelines; classifying unstructured texts and images; genAI model pipelines Whether you currently work to create products and services that use machine learning, or would like to in the future, this practical book teaches you the basics and advanced aspects of the production ML lifecycle. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
- Softcover
Seller: Rarewaves USA United, OSWEGO, IL, U.S.A.Rarewaves USA United
Contact seller5-star sellerCondition: New
£ 53.18
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Paperback. Condition: New. Using machine learning for products, services, and critical business processes is quite different from using ML in an academic or research setting-especially for recent ML graduates and those moving from research to a commercial environment. Whether you currently work to create products and services th…at use ML, or would like to in the future, this practical book gives you a broad view of the entire field.Authors Robert Crowe, Hannes Hapke, Emily Caveness, and Di Zhu help you identify topics that you can dive into deeper, along with reference materials and tutorials that teach you the details. You'll learn the state of the art of machine learning engineering, including a wide range of topics such as modeling, deployment, and MLOps. You'll learn the basics and advanced aspects to understand the production ML lifecycle.This book provides four in-depth sections that cover all aspects of machine learning engineering:Data: collecting, labeling, validating, automation, and data preprocessing; data feature engineering and selection; data journey and storageModeling: high performance modeling; model resource management techniques; model analysis and interoperability; neural architecture searchDeployment: model serving patterns and infrastructure for ML models and LLMs; management and delivery; monitoring and loggingProductionalizing: ML pipelines; classifying unstructured texts and images; genAI model pipelines.
- Softcover
Seller: moluna, Greven, Germanymoluna
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£ 60.32
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Condition: New. Über den AutorRobert Crowe is a data scientist and TensorFlow enthusiast. Robert has a passion for helping developers quickly learn what they need to be productive. Robert is the Senior Product Manager for TensorFlow Open-Source and.
- Softcover
Seller: Rarewaves.com UK, London, United KingdomRarewaves.com UK
Contact seller5-star sellerCondition: New
£ 54.48
£ 65.00 shippingShips from United Kingdom to U.S.A.Quantity: 2 available
Paperback. Condition: New. Using machine learning for products, services, and critical business processes is quite different from using ML in an academic or research setting-especially for recent ML graduates and those moving from research to a commercial environment. Whether you currently work to create products and services th…at use ML, or would like to in the future, this practical book gives you a broad view of the entire field.Authors Robert Crowe, Hannes Hapke, Emily Caveness, and Di Zhu help you identify topics that you can dive into deeper, along with reference materials and tutorials that teach you the details. You'll learn the state of the art of machine learning engineering, including a wide range of topics such as modeling, deployment, and MLOps. You'll learn the basics and advanced aspects to understand the production ML lifecycle.This book provides four in-depth sections that cover all aspects of machine learning engineering:Data: collecting, labeling, validating, automation, and data preprocessing; data feature engineering and selection; data journey and storageModeling: high performance modeling; model resource management techniques; model analysis and interoperability; neural architecture searchDeployment: model serving patterns and infrastructure for ML models and LLMs; management and delivery; monitoring and loggingProductionalizing: ML pipelines; classifying unstructured texts and images; genAI model pipelines.
- Softcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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£ 71.45
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Taschenbuch. Condition: Neu. Neuware - Using machine learning for products, services, and critical business processes is quite different from using ML in an academic or research setting--especially for recent ML graduates and those moving from research to a commercial environment. Whether you currently work to create products an…d services that use ML, or would like to in the future, this practical book gives you a broad view of the entire field. Authors Robert Crowe, Hannes Hapke, Emily Caveness, and Di Zhu help you identify topics that you can dive into deeper, along with reference materials and tutorials that teach you the details. You'll learn the state of the art of machine learning engineering, including a wide range of topics such as modeling, deployment, and MLOps. You'll learn the basics and advanced aspects to understand the production ML lifecycle. This book provides four in-depth sections that cover all aspects of machine learning engineering: - Data: collecting, labeling, validating, automation, and data preprocessing; data feature engineering and selection; data journey and storage - Modeling: high performance modeling; model resource management techniques; model analysis and interoperability; neural architecture search - Deployment: model serving patterns and infrastructure for ML models and LLMs; management and delivery; monitoring and logging - Productionalizing: ML pipelines; classifying unstructured texts and images; genAI model pipelines.
Machine Learning Production Systems: Engineering Machine Learning Models and Pipelines
Crowe, Robert/ Hapke, Hannes/ Caveness, Emily/ Zhu, Di/ Nelson, Catherine
- Softcover
- Print on Demand
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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£ 61.22
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Paperback. Condition: Brand New. 2nd edition. 260 pages. 9.19x7.00x9.19 inches. In Stock. This item is printed on demand.
