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Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications
book

Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications

by Andrew Kelleher, Adam Kelleher
May 2019
Beginner to intermediate
288 pages
9h 1m
English
Addison-Wesley Professional
Content preview from Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications

About the Authors

Andrew Kelleher is a staff software engineer and distributed systems architect at Venmo. He was previously a staff software engineer at BuzzFeed and has worked on data pipelines and algorithm implementations for modern optimization. He graduated with a BS in physics from Clemson University. He runs a meetup in New York City that studies the fundamentals behind distributed systems in the context of production applications, and was ranked one of FastCompany’s most creative people two years in a row.

Adam Kelleher wrote this book while working as principal data scientist at BuzzFeed and adjunct professor at Columbia University in the City of New York. As of May 2018, he is chief data scientist for research at Barclays and teaches ...

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Publisher Resources

ISBN: 9780134116556