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The Data Side of Big Data: Crowdsourcing Content and Smart Systems

  • Jun 7, 2013 · 12:30 PM
  • USF

Speaker: Shane Lewin with John Clark

The field of machine learning has advanced to the point that out of the box solutions are sufficient to solve 99% of big data problems.  Yet many solutions never see the light of day due to the high cost of acquiring quality data in volume.  A number of products have emerged to meet this demand, ranging from algorithmic solutions to crowdsourcing to hybrid models, each with distinct advantages and limitations.  In this talk I will survey some of the advances in data collection and discuss their strengths and limitations.  Finally I will argue that smart systems, which combine data handling algorithms and machine learning in a self reinforcing system, represent the future and the biggest opportunity in data sciences in the next decade.

Shane has architected half a dozen smart systems, ranging from automated customer feedback to the web-scale machine learned ranker that powers all of the text you see on Bing.  He currently managing a Data Science and Engineering team at Netflix, where he continues to be neurotically obsessed with data quality.

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  • A former member
    A former member

    @Sophia (or anyone else), feel free to reach out if I can answer any questions for you, either here or via slewin at



    June 18, 2013

  • Kirsten K.

    Slides from this talk are now available in the files section here:

    June 17, 2013

  • Russell W.

    Apologies for bowing out: meeting at work.

    June 7, 2013

  • Niels J.

    Please update your RSVP if you don't think you can attend on Friday at 12:30. Thanks!

    June 5, 2013

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