Building Machine Learning Pipelines

Hannes Hapke

ISBN 10: 1492053198 / ISBN 13: 9781492053194

Published by O'reilly Media Aug 2020, 2020

About this Item

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

Bibliographic Details

• Title: Building Machine Learning Pipelines
• Publisher: O'reilly Media Aug 2020
• Publication Date: 2020
• Language: English
• ISBN 10: 1492053198
• ISBN 13: 9781492053194
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 232W x 179H x 23D millimeters 232 millimeters width by 179 millimeters height by 23 millimeters depth
• Weight: 652 grams

About this title

Synopsis

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

About the Author

Hannes Hapke is a senior data scientist for Concur Labs at SAP Concur, where he explores innovative ways to use machine learning to improve the experience of a business traveller. Prior to joining SAP Concur, Hannes solved machine learning infrastructure problems in various industries including healthcare, retail, recruiting and renewable energies. Additionally, Hannes has co-authored a publication about natural language processing and deep learning and presented at various conferences about deep learning and Python. He holds a Master of Science in Electrical Engineering from Oregon State University.

Catherine Nelson is also 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. Her key focus areas range from ML explainability and model analysis to privacy-preserving ML. 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.