ISBN: 9781098156015 - machine learning production systems: engineering machine learning models and pipelines (30 resultados)

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Paperback. Condición: 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 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 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.…

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Paperback. Condición: 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.…

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Paperback. Condición: 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 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 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.…

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Paperback. Condición: 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 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 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.…

Taschenbuch. Condición: 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 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.…

Condición: New. &Uumlber 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.

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Paperback. Condición: 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.…

Paperback. Condición: 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 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 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.…

Taschenbuch. Condición: Neu. Machine Learning Production Systems | Engineering Machine Learning Models and Pipelines | Di Zhu (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2024 | O'Reilly Media | EAN 9781098156015 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu. …

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Paperback. Condición: Brand New. 2nd edition. 260 pages. 9.19x7.00x9.19 inches. In Stock. This item is printed on demand.

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