Across industries, machine learning drives value – building revenue, cutting costs and making organizations more competitive. But machine learning can be challenging to implement: data scientists are scarce, and many analytics teams lack the right tools for today’s big, complex and fast data.

In this paper, we discuss:

  • Why so many data scientists say they could be more productive than they are today.
  • Why companies pay data scientists top dollar – then fail to provide the right tools.
  • What data scientists do most of the time. (Hint: it’s not machine learning).
  • The key components of an agile platform for machine learning.
  • Simple steps you can take to build a high-performance data science team. 

Download the paper to learn more.

ABOUT THE AUTHOR:

Thomas W. Dinsmore is a consultant and author who specializes in machine learning; he has contributed to analytic projects for more than 500 clients around the world.

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