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Kubeflow for Machine Learning: From Lab to Production by [Trevor Grant, Holden Karau, Boris Lublinsky, Richard Liu, Ilan Filonenko]

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Kubeflow for Machine Learning: From Lab to Production 1st Edition, Kindle Edition

4.1 out of 5 stars 23 ratings

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About the Author

Trevor Grant is a member of the Apache Software Foundation, and is heavily involved in the Apache Mahout, Apache Streams, and Community Development projects. He often tinkers and occasionally documents his (mis)adventures at www.rawkintrevo.org. In the before time, he was an international speaker on technology, but now he focuses mainly on writing. Trevor wishes to thank IBM for their continued patronage of his artistic endeavors. He lives in Chicago because it's the best city on the planet, with world class food, parks, and culture, and because the skies are never orange.



Holden Karau is a queer transgender Canadian, Apache Spark committer, Apache Software Foundation member, and an active open source contributor. She also extends her passion for building community with industry projects including Scaling for Python for ML and teaching distributed computing to children. As a software engineer, she's worked on a variety of distributed compute, search, and classification problems at Google, IBM, Alpine, Databricks, Foursquare, and Amazon. She graduated from the University of Waterloo with a bachelor of mathematics in computer science. Outside of software she enjoys playing with fire, welding, riding scooters, eating poutine, and dancing.



Boris Lublinsky is a Principal Architect at Lightbend. Boris has over 25 years experience in enterprise, technical architecture, and software engineering. He is an active member of OASIS SOA RM committee, co-author of Applied SOA: Service-Oriented Architecture and Design Strategies (Wiley) and author of numerous articles on Architecture, Programming, Big Data, SOA and BPM.



Richard Liu is a Senior Software Engineer at Waymo, where he focuses on building a machine learning platform for self-driving cars. Previously he has worked at Microsoft Azure and Google Cloud. He is one of the primary maintainers of the Kubeflow project and has given several talks at KubeCon. He holds a Master's degree in Computer Science from University of California, San Diego.



Ilan Filonenko is a member of the Data Science Infrastructure team at Bloomberg, where he has designed and implemented distributed systems at both the application and infrastructure level. Previously, Ilan was an engineering consultant and technical lead in various startups and research divisions across multiple industry verticals, including medicine, hospitality, finance, and music. He actively contributes to open source, primarily Apache Spark and Kubeflow's KFServing. He is one of the principal contributors to Spark on Kubernetes--primarily focusing on remote shuffle and HDFS security, and to multi-model serving in KFServing. Ilan's research has been in algorithmic, software, and hardware techniques for high-performance machine learning with a focus on optimizing stochastic algorithms and model management.

--This text refers to the paperback edition.

Product details

  • ASIN ‏ : ‎ B08L5Q9W59
  • Publisher ‏ : ‎ O'Reilly Media; 1st edition (13 October 2020)
  • Language ‏ : ‎ English
  • File size ‏ : ‎ 11779 KB
  • Simultaneous device usage ‏ : ‎ Unlimited
  • Text-to-Speech ‏ : ‎ Enabled
  • Enhanced typesetting ‏ : ‎ Enabled
  • X-Ray ‏ : ‎ Not Enabled
  • Word Wise ‏ : ‎ Not Enabled
  • Print length ‏ : ‎ 264 pages
  • Customer Reviews:
    4.1 out of 5 stars 23 ratings

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4.1 out of 5 stars
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Thaïs
5.0 out of 5 stars Great exposition into Kubeflow
Reviewed in the United Kingdom on 3 December 2020
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p ede
1.0 out of 5 stars Not recommended. Reads like library docs not like a book
Reviewed in Germany on 1 March 2021
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Darklinux
4.0 out of 5 stars The step after kubernetes
Reviewed in France on 14 May 2021
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Kzero
5.0 out of 5 stars A much more complete picture of ML in production.
Reviewed in the United States on 21 November 2020
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Marie L.
3.0 out of 5 stars Great content, disappointing print (black and white images and code)
Reviewed in the United States on 26 April 2021
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