May 2019
Beginner to intermediate
288 pages
9h 1m
English
book
Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications
by Andrew Kelleher, Adam Kelleher
Content preview from Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications
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Credits
Cover: Digital technology web banner, big data machine learning algorithms, abstract banner analysis of information, isometric view, science dark blue background by Svetlana Avv/Shutterstock.
Chapter 2: “Build domain knowledge . . . refine existing infrastructure”. M. Herman, S. Rivera, S. Mills, J. Sullivan, P. Guerra, A. Cosmas, D. Farris, E. Kohlwey, P. Yacci, B. Keller, A. Kherlopian, and M. Kim, The Field Guide to Data Science. Nov. 2013.
Chapter 2, Figure 2.2: M. Herman, S. Rivera, S. Mills, J. Sullivan, P. Guerra, A. Cosmas, D. Farris, E. Kohlwey, P. Yacci, B. Keller, A. Kherlopian, and M. Kim, The Field Guide to Data Science. Nov. 2013.
Chapter 2: “Assess project requirements . . . incorporating new information”. M. F. Smith, ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.