One of the most exciting areas in Big Data is the development of new predictive applications; apps used to drive product recommendations, predict machine failures, forecast airfare, social match-make, identify fraud, predict disease outbreaks, and repurpose pharmaceuticals. These applications output real-time predictions and recommendations in response to user and machine input to directly derive business value and create cool experiences. These hold the true promise of Big Data.
The most interesting apps utilize multiple types of data (tables, graphs, text, & images) in a creative way. Typically, these are developed using data that’s larger than single machine memory, but smaller than the Pb’s some companies brag about housing. This “Medium Data” regime of >5Gb and <10Tb is where data science magic happens. In this talk, I’ll share the trends we’re seeing in predictive application development, show how to build and deploy a predictive app that exploits the power of combining different data types and representations (like graphs and tables), and through customer case studies share some key lessons data scientists and developers should like to hear.
Shawn is the Director of Product at GraphLab where he helps make it easy to build cool experiences with data. He is data geeky and loves inspired technologies, businesses, and gadgets. His technical background spans recommendation systems and business analytics, physics simulations, and energy. He holds a PhD in Materials Science from Stanford University and a BA in Physics from Cornell University.
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