Abstract: There are many challenges in building an end to end data solution. People run into issues around data cleansing and manipulation, scaling their models to a production environment, managing their models over time, and connecting their scoring service to other components of their solution. Azure Machine Learning Workbench, a new tool unveiled by Microsoft just a few weeks ago, simplifies and helps deal with these issues.
This workshop will introduce and walk through Azure Machine Learning Workbench. We will talk about some of the biggest challenges for Data Scientists, and look at ways in which Workbench solves these problems. Students will learn how to use Workbench and walk through labs designed to emulate real world customer scenarios, taken from our collective experience.
Bio: Sid Ramesh builds end to end Machine Learning solutions for partners and customers for a variety of industries including healthcare, retail, manufacturing, and finance. His areas of expertise are R, SQL, and econometrics. Sid graduated from University of Southern California in Economics and Statistics, and is currently pursuing his MS at Carnegie Mellon University. Currently, Sid is a Data Scientist at Microsoft and has previously worked at Revolution Analytics as an R Consultant and Trainer.