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Cover: 9781492053194 | Building Machine Learning Pipelines | Hannes Hapke (u. a.) | Buch
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Building Machine Learning Pipelines
Automating Model Life Cycles with Tensorflow
Taschenbuch von Hannes Hapke (u. a.)
Sprache: Englisch

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Beschreibung
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. The book also explores new approaches for integrating data privacy into machine learning pipelines. Understand the machine learning management lifecycle Implement data pipelines; Build your pipeline using components from TensorFlow extended; Orchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow Data Validation and TensorFlow Transform; Analyze a model in detail using TensorFlow model analysis; Examine fairness and bias in your model performance; Deploy models with TensorFlow serving or TensorFlow Lite for mobile devices; Learn privacy-preserving machine learning techniques.
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. The book also explores new approaches for integrating data privacy into machine learning pipelines. Understand the machine learning management lifecycle Implement data pipelines; Build your pipeline using components from TensorFlow extended; Orchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow Data Validation and TensorFlow Transform; Analyze a model in detail using TensorFlow model analysis; Examine fairness and bias in your model performance; Deploy models with TensorFlow serving or TensorFlow Lite for mobile devices; Learn privacy-preserving machine learning techniques.
Details
Erscheinungsjahr: 2020
Medium: Taschenbuch
Inhalt: Einband - flex.(Paperback)
ISBN-13: 9781492053194
ISBN-10: 1492053198
Sprache: Englisch
Einband: Kartoniert / Broschiert
Autor: Hannes Hapke
Catherine Nelson
Hersteller: O'Reilly Media
Verantwortliche Person für die EU: preigu, Ansas Meyer, Lengericher Landstr. 19, D-49078 Osnabrück, mail@preigu.de
Abbildungen: Illustrations, unspecified
Maße: 232 x 177 x 23 mm
Von/Mit: Hannes Hapke (u. a.)
Erscheinungsdatum: 28.07.2020
Gewicht: 0,636 kg
Artikel-ID: 121105498
Details
Erscheinungsjahr: 2020
Medium: Taschenbuch
Inhalt: Einband - flex.(Paperback)
ISBN-13: 9781492053194
ISBN-10: 1492053198
Sprache: Englisch
Einband: Kartoniert / Broschiert
Autor: Hannes Hapke
Catherine Nelson
Hersteller: O'Reilly Media
Verantwortliche Person für die EU: preigu, Ansas Meyer, Lengericher Landstr. 19, D-49078 Osnabrück, mail@preigu.de
Abbildungen: Illustrations, unspecified
Maße: 232 x 177 x 23 mm
Von/Mit: Hannes Hapke (u. a.)
Erscheinungsdatum: 28.07.2020
Gewicht: 0,636 kg
Artikel-ID: 121105498
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