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Big Data Science Practice + Algo Implementation


By Team | minute read | May 10, 2013

Category: Uncategorized
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In this double header we present a practitioners close view of the science and an engineer’s close view of design and implementation of distributed algorithm.

Day in the Life of a Data Scientist – Chris Pouliot 
In this session, Netflix
analytical leader Chris Pouliot shares his experience building a large team of data scientists at Netflix and what a typical day in life of a Data Scientist looks like. From extracting and exploring data to posing good questions around them and matching them with the right algorithms, Chris goes through the lifecycle of data science  in practice.
Chris built a central, horizontal team for the company that spans across all business verticals. Chris shares insights and stories, covering pitfalls and successes and impact they have at Netflix.
Distributed Generalized Linear Modeling (GLM) –
Tomas Nykodym
In this session, 0xdata engineers, Tomas Nykodym & Cliff Click explain how to build a Distributed GLM (Logistic, Poisson Regression .) Generalized_linear_model is the most popular tool at the hand of a good datascientist. A couple of very powerful mathematical approaches such as Stephen Boyd’s ADMM and Generalized Gradients are analyzed along with implementation choices. Live Demo and performance comparisons between the two approaches and on applications on Big Data will be presented.

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At, democratizing AI isn’t just an idea. It’s a movement. And that means that it requires action. We started out as a group of like minded individuals in the open source community, collectively driven by the idea that there should be freedom around the creation and use of AI. Today we have evolved into a global company built by people from a variety of different backgrounds and skill sets, all driven to be part of something greater than ourselves. Our partnerships now extend beyond the open-source community to include business customers, academia, and non-profit organizations.