Navdeep Gill is a Senior Data Scientist/Software Engineer at H2O.ai where he focuses mainly on machine learning interpretability and previously focused on GPU accelerated machine learning, automated machine learning, and the core H2O-3 platform.
Prior to joining H2O.ai, Navdeep worked at Cisco focusing on data science and software development. Before that Navdeep was a researcher/analyst in several neuroscience labs at the following institutions: California State University, East Bay, University of California, San Francisco, and Smith Kettlewell Eye Research Institute.
Navdeep graduated from California State University, East Bay with a M.S. in Computational Statistics, a B.S. in Statistics, and a B.A. in Psychology (minor in Mathematics).
The rsparkling R package is an extension package for sparklyr (an R interface for Apache Spark) that creates an R front-end for the Sparkling Water Spark package from H2O. This provides an interface to H2O's high performance, distributed machine learning algorithms on Spark, using R. The main purpose of this package is to provide a connector between sparklyr and H2O's machine learning algorithms. In this session, Gill will introduce the basic architectures of rsparkling, H2O Sparkling Water and sparklyr, and go over how these frameworks work together to build a cohesive machine learning framework. In addition, you'll learn about various implementations for using rsparkling in production. The session will conclude with a live demo of rsparkling that will display an end-to-end use case of data ingestion, munging and machine learning. Session hashtag: #SFdev15