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Perspectives on Becoming an Applied Machine Learning Scientist.

While becoming a scientist is all about creating a dent in the boundaries of human knowledge, applying that expertise in a corporate setting requires a transformation of the scientist skill set. In both academia and industry, the primary focus is problem solving. In academia, however, it is targeted...

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Published in:Computer (00189162) Vol. 52; no. 5; pp. 40 - 48
Main Author: Rasiwasia, Nikhil
Format: Article
Published: IEEE May2019
Subjects:
Machine learning
Scientists
Systems on a chip
Graphics processing units
Online Access:View this record in EBSCOhost
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        au: Rasiwasia, Nikhil
        affil: India Machine Learning, Amazon, Bangalore, India
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        Machine learning
        Scientists
        Systems on a chip
        Graphics processing units
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          Machine learning
          Scientists
          Systems on a chip
          Graphics processing units
      keyword:
        Companies
        Data models
        Measurement
        White spaces
      ab: While becoming a scientist is all about creating a dent in the boundaries of human knowledge, applying that expertise in a corporate setting requires a transformation of the scientist skill set. In both academia and industry, the primary focus is problem solving. In academia, however, it is targeted toward creating a novel solution that pushes the state of the art, while, in industry, the solution is valuable if it leads to the desired change in business metrics.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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