April 2017
Intermediate to advanced
532 pages
12h 39m
English
book
Machine Learning with Spark - Second Edition
by Rajdeep Dua, Brian O'Neill, Stephen Boesch, Manpreet Singh Ghotra, Nick Pentreath
Content preview from Machine Learning with Spark - Second Edition
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Summary
In this chapter, we learnt about the components that are inherent in a data-driven, automated machine learning system. We also outlined how a possible high-level architecture for such a system might look in a real-world situation. We also got an overview of MLlib-Spark's machine learning library-compared to other machine learning implementations from a performance perspective. In the end, we looked at new features in various versions of Spark starting from Spark 1.6 to Spark 2.0.
In next chapter, we shall discuss how to obtain publicly-available datasets for common machine learning tasks. We will also explore general concepts to process, clean, and transform data so that it can be used to train a machine learning model.
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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.