Machine Learning Production Systems, 9781098156015
Paperback
From research to real world: Master machine learning production systems.

Machine Learning Production Systems

engineering machine learning models and pipelines

$154.36

  • Paperback

    260 pages

  • Release Date

    15 October 2024

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Summary

From Research to Reality: Mastering Machine Learning Production Systems

Using machine learning for real-world products, services, and key business operations is a different ball game than academic or research environments. This is especially true for recent ML grads and those transitioning from research to commercial settings. Whether you’re already building ML-powered products or aspire to, this practical guide provides a comprehensive overview of the field.

Authors Robert …

Book Details

ISBN-13:9781098156015
ISBN-10:1098156013
Author:Robert Crowe, Hannes Hapke, Emily Caveness, Di Zhu
Publisher:O'Reilly Media
Imprint:O'Reilly Media
Format:Paperback
Number of Pages:260
Edition:2nd
Release Date:15 October 2024
Weight:814g
Dimensions:233mm x 178mm
About The Author

Robert Crowe

Robert Crowe is a data scientist and TensorFlow enthusiast with a passion for helping developers quickly learn what they need to be productive. Robert is the Senior Product Manager for TensorFlow Open-Source and MLOps at Google and helps ML teams meet the challenges of creating products and services with ML. Previously, Robert led software engineering teams for both large and small companies, always focusing on clean, elegant solutions to well-defined needs.

Hannes Hapke is a Senior Machine Learning Engineer at Digits, and has co-authored multiple machine learning publications, including the book “Building Machine Learning Pipelines” by O’Reilly Media. He has also presented state-of-the-art ML work at conferences like ODSC or O’Reilly’s TensorFlow World and is an active contributor to TensorFlow’s TFX Addons project. Hannes is passionate about machine learning engineering and production machine learning use cases using the latest machine learning developments.

Emily Caveness is a software engineer at Google. She currently works on ML data analysis and validation.

Di Zhu is an engineer at Google. She has worked on a variety of projects, including MLOps infrastructure, applied machine learning solutions for different verticals including vision, ranking, dynamic pricing, etc. She is passionate about using engineering to solve real-world problems, designing and delivering MLOps solutions for several critical Google products and external partners. In addition to professional pursuits, Di is also a tennis player, Latin dancing competitor, and piano player.

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