Building a scalable data science platform with R

Hadoop is famously scalable, as is cloud computing. R, the thriving and extensible open source data science software. . .not so much. Mario Inchiosa and Roni Burd outline how to seamlessly combine Hadoop, cloud computing, and R to create a scalable data science platform that lets you explore, transform, model, and score data at any scale from the comfort of your favorite R environment.

Hadoop is famously scalable, as is cloud computing. R, the thriving and extensible open source data science software. . .not so much. But what if we seamlessly combined Hadoop, cloud computing, and R to create a scalable data science platform? Imagine exploring, transforming, modeling, and scoring data at any scale from the comfort of your favorite R environment. Now, imagine calling a simple R function to operationalize your predictive model as a scalable, cloud-based web services API. Mario Inchiosa and Roni Burd demonstrate how to use the magic of the cloud to run your R code, thousands of open source R extension packages, and distributed implementations of the most popular machine-learning algorithms at scale. This session is sponsored by Microsoft.