Building Machine Learning Pipelines
Language: English
Published by O'reilly Media Aug 2020, 2020
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Add to basketItem description from seller
Neuware - 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.…
Seller Inventory # 9781492053194
- Title
- Building Machine Learning Pipelines
- Author
- Hannes Hapke
- Publisher
- O'reilly Media Aug 2020
- Publication year
- 2020
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1492053198
- ISBN 13
- 9781492053194
- Item weight
- 652 grams
- Dimensions
- 232x179x23 mm
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 pipeline
- Build your pipeline using components from TensorFlow Extended
- Orchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow Pipelines
- Work with data using TensorFlow 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
"Synopsis" may belong to another edition of this title.
About the Author
Catherine Nelson is a senior data scientist for Concur Labs at SAP Concur, where she explores innovative ways to use machine learning to improve the experience of a business traveller. She is particularly interested in privacy-preserving ML and applying deep learning to enterprise data. In her previous career as a geophysicist she studied ancient volcanoes and explored for oil in Greenland. Catherine has a PhD in geophysics from Durham University and a Masters of Earth Sciences from Oxford University.
"About the title" may belong to another edition of this title.
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