Building Machine Learning Pipelines: Automating Model Life Cycles with TensorFlow

Author:

Hannes Hapke

Publisher:

Shroff/O'Reilly

Rs1425

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Publisher

Shroff/O'Reilly

Publication Year 2020
ISBN-13

9789385889004

ISBN-10 9789385889004
Binding

Paperback

Number of Pages 368 Pages
Language (English)
Dimensions (Cms) 20X14X4
Weight (grms) 600
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

Hannes Hapke

Hannes is a Senior Machine Learning Engineer at SAP Concur where he focuses on ML Infrastructure and Natural Language Processing projects. Hannes is a Google Developer Expert for Machine Learning and a co-author of machine learning publications like NLP in Action
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