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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7. Introduction to Algorithms and Architectures
7.1 Introduction
You have most of the background in place now and can finally start machine learning! There are two main settings in which you’ll run machine-learning applications. The first setting is on a local computer, or workstation. This is usually for the purposes of a small, one-off analysis. The second context is in a production environment where a task is automated with relatively long-term support.
An example of a small, one-off analysis would be to find terms in article titles that correlate with higher click rates. You might do this analysis to report to a team of writers so they can experiment with the terms in their titles to increase click rates. An example of something that might ...
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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.