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Effective Data Science Infrastructure: How to make data scientists productive

4.6 out of 5 stars 8 ratings

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  • Effective Data Science Infrastructure: How to make data scientists productive
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  • Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications
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Editorial Reviews

Review

"By reading and referring to this book, I'm confident you will learn how to make your machine learning operations much more efficient and productive."
From the Foreword by Travis Oliphant, Author of NumPy, Founder of
Anaconda, PyData, and NumFOCUS


"Effective Data Science Infrastructure is a brilliant book. It's a must-have for every data science team."
—Ninoslav Cerkez, Logit
 
"More data science. Less headaches."
—Dr. Abel Alejandro Coronado Iruegas, National Institute of
Statistics and Geography of Mexico


"Indispensable. A copy should be on every data engineer's bookshelf."
—Matthew Copple, Grand River Analytics

About the Author

Ville Tuulos has been developing tools and infrastructure for data science and machine learning for over two decades. At Netflix, he designed and built Metaflow, a full-stack framework for data science. Currently, he is the CEO of a startup focusing on data science infrastructure.

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Product details

  • Publisher ‏ : ‎ Manning (August 16, 2022)
  • Language ‏ : ‎ English
  • Paperback ‏ : ‎ 352 pages
  • ISBN-10 ‏ : ‎ 1617299197
  • ISBN-13 ‏ : ‎ 978-1617299193
  • Item Weight ‏ : ‎ 1.35 pounds
  • Dimensions ‏ : ‎ 7.38 x 0.8 x 9.25 inches
  • Customer Reviews:
    4.6 out of 5 stars 8 ratings

About the author

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Ville has been developing infrastructure for machine learning for over two decades. He has worked as an ML researcher in academia and as a leader at a number of companies, including Netflix where he led the ML infrastructure team that created Metaflow, a popular open-source framework for data science infrastructure. He is a co-founder and CEO of Outerbounds, a company developing modern human-centric ML.

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4.6 out of 5 stars
4.6 out of 5
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