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Building Machine Learning Pipelines by Hapke Hannes (36 results)
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Condition: Very Good. Item in very good condition! Textbooks may not include supplemental items i.e. CDs, access codes etc.
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Building Machine Learning Pipelines: Automating Model Life Cycles with TensorFlow
Published by O'Reilly Media (edition 1), 2020
ISBN 10: 1492053198 ISBN 13: 9781492053194
Language: English
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Building Machine Learning Pipelines: Automating Model Life Cycles with Tensorflow (Paperback or Softback)
Published by O'Reilly Media 8/4/2020, 2020
ISBN 10: 1492053198 ISBN 13: 9781492053194
Language: English
Paperback or Softback. Condition: New. Building Machine Learning Pipelines: Automating Model Life Cycles with Tensorflow 1.28. Book.
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Building Machine Learning Pipelines
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Paperback. Condition: New. Companies are spending billions on machine learning projects, but it's money wasted if the models can't be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You'll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems.Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects.Understand the steps to build a machine learning pipelineBuild your pipeline using components from TensorFlow ExtendedOrchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow PipelinesWork with data using TensorFlow Data Validation and TensorFlow TransformAnalyze a model in detail using TensorFlow Model AnalysisExamine fairness and bias in your model performanceDeploy models with TensorFlow Serving or TensorFlow Lite for mobile devicesLearn privacy-preserving machine learning techniques.
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Building Machine Learning Pipelines (Paperback)
Published by O'Reilly Media, Sebastopol, 2020
ISBN 10: 1492053198 ISBN 13: 9781492053194
Language: English
Paperback. Condition: new. Paperback. Companies are spending billions on machine learning projects, but it's money wasted if the models can't be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You'll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems.Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects.Understand the steps to build a machine learning pipelineBuild your pipeline using components from TensorFlow ExtendedOrchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow PipelinesWork with data using TensorFlow Data Validation and TensorFlow TransformAnalyze a model in detail using TensorFlow Model AnalysisExamine fairness and bias in your model performanceDeploy models with TensorFlow Serving or TensorFlow Lite for mobile devicesLearn privacy-preserving machine learning techniques Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Building Machine Learning Pipelines : Automating Model Life Cycles With TensorFlow
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Building Machine Learning Pipelines: Automating Model Life Cycles with TensorFlow
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Building Machine Learning Pipelines: Automating Model Life Cycles with Tensorflow
Published by O'Reilly (WILEY UK) 2020-07-28, 2020
ISBN 10: 1492053198 ISBN 13: 9781492053194
Language: English
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Building Machine Learning Pipelines: Automating Model Life Cycles with Tensorflow
Published by O'Reilly Media, Inc, USA, 2020
ISBN 10: 1492053198 ISBN 13: 9781492053194
Language: English
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Building Machine Learning Pipelines: Automating Model Life Cycles with Tensorflow
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Building Machine Learning Pipelines : Automating Model Life Cycles With TensorFlow
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Building Machine Learning Pipelines
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Add to basketPaperback. Condition: New. Companies are spending billions on machine learning projects, but it's money wasted if the models can't be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You'll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems.Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects.Understand the steps to build a machine learning pipelineBuild your pipeline using components from TensorFlow ExtendedOrchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow PipelinesWork with data using TensorFlow Data Validation and TensorFlow TransformAnalyze a model in detail using TensorFlow Model AnalysisExamine fairness and bias in your model performanceDeploy models with TensorFlow Serving or TensorFlow Lite for mobile devicesLearn privacy-preserving machine learning techniques.
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Add to basketPaperback. Condition: New. Companies are spending billions on machine learning projects, but it's money wasted if the models can't be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You'll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems.Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects.Understand the steps to build a machine learning pipelineBuild your pipeline using components from TensorFlow ExtendedOrchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow PipelinesWork with data using TensorFlow Data Validation and TensorFlow TransformAnalyze a model in detail using TensorFlow Model AnalysisExamine fairness and bias in your model performanceDeploy models with TensorFlow Serving or TensorFlow Lite for mobile devicesLearn privacy-preserving machine learning techniques.
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Building Machine Learning Pipelines: Automating Model Life Cycles With Tensorflow
Published by Oreilly & Associates Inc, 2020
ISBN 10: 1492053198 ISBN 13: 9781492053194
Language: English
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Add to basketPaperback. Condition: Brand New. 337 pages. 8.75x6.75x0.75 inches. In Stock.
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Add to basketKartoniert / Broschiert. Condition: New. Über den AutorrnrnHannes Hapke is a VP of Engineering at Caravel, a machine learning company providing novel personalization products for the retail industry. Prior to joining Caravel, Hannes was a Ssenior data science engineer at C.
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Building Machine Learning Pipelines (Paperback)
Published by O'Reilly Media, Sebastopol, 2020
ISBN 10: 1492053198 ISBN 13: 9781492053194
Language: English
US$ 114.07
Convert currencyUS$ 37.00 shipping from Australia to U.S.A.Quantity: 1 available
Add to basketPaperback. Condition: new. Paperback. Companies are spending billions on machine learning projects, but it's money wasted if the models can't be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You'll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems.Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects.Understand the steps to build a machine learning pipelineBuild your pipeline using components from TensorFlow ExtendedOrchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow PipelinesWork with data using TensorFlow Data Validation and TensorFlow TransformAnalyze a model in detail using TensorFlow Model AnalysisExamine fairness and bias in your model performanceDeploy models with TensorFlow Serving or TensorFlow Lite for mobile devicesLearn privacy-preserving machine learning techniques Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
