Presented By O'Reilly and Cloudera
Make Data Work
March 28–29, 2016: Training
March 29–31, 2016: Conference
San Jose, CA

Education conference sessions

11:50am–12:30pm Thursday, 03/31/2016
Roshan Sumbaly (Facebook), Pierre Barthelemy (Coursera)
Coursera's platform allows 15 million learners to take courses from the best universities. Roshan Sumbaly and Thomas Barthelemy outline the pieces of Coursera's data infrastructure (streaming, data warehouse) that support its growing semi- and unstructured data requirements and explain how this ecosystem allows Coursera to build various instructor- and learner-side data products.
11:00am–11:40am Tuesday, 03/29/2016
Matthew Gee (Impact Lab/University of Chicago )
Machine-learning algorithms are the workhorses of the data economy but often seem like one part math and two parts magic. Matthew Gee demystifies the core concepts of machine learning, gives practical examples of applications, and walks attendees through some basic rules for deciding if your organization’s key questions and data sources are a good fit for a machine-learning solution.
2:40pm–3:20pm Wednesday, 03/30/2016
Robert Grossman (University of Chicago)
There is a big difference between running a machine-learning algorithm manually from time to time and building a production system that runs thousands of machine-learning algorithms each day on petabytes of data, while also dealing with all the edge cases that arise. Robert Grossman discusses some of the lessons learned when building such a system and explores the tools that made the job easier.