San Francisco, California
London, United Kingdom
This tech talk will describe how to build an experiment platform that can handle large-scale experiments. The talk will also discuss several best practices in designing and analyzing online experiments learned from companies like Microsoft and LinkedIn.
About the Speakers
Ya Xu has been working in the domain of online A/B testing for over 4 years. She currently leads a team of engineers and data scientists building a world-class online A/B testing platform at LinkedIn. She also spearheads taking LinkedIn's A/B testing culture to the next level by evangelizing best practices and pushing for broad-based platform adoption. She holds a Ph.D. in Statistics from Stanford University.
Chuong (Tom) Do currently leads a team of data engineers and analysts in the Analytics team at Coursera, which is responsible for data infrastructure and quantitative analysis in support of the product and business. He completed his Ph.D. in Computer Science at Stanford University in 2009 and worked as a scientist in the personal genetics company 23andMe until 2012, where his research has collectively spanned the fields of machine learning, computational biology, and statistical genetics.
When & Where
This talk is hosted by Coursera. We're an education technology platform that serves the best courses from top universities to millions of learners around the world for free. We embrace learning at scale: our platform features massive courses to hundred thousands of students, automated and crowd-sourced grading, and advanced identity verification.
We run 100% on AWS and use a variety of technologies in our platform like Backbone.js, Python, Scala, Play, Cassanda, and more.