Scalable data science and deep learning with R
Scalable data science and deep learning with R

Abstract: 

We provide an overview of the tools available to data scientists using R for Spark and TensorFlow, then discuss the latest developments at the intersections of these ecosystems. We organize the conversation around a diverse selection of use cases, such as ad hoc analysis on distributed datasets, building machine learning models for low latency scoring, and developing deep learning models for research, and demonstrate sample workflows. Various open source R packages will be featured, including the sparklyr, keras, and tensorflow projects.

Bio: 

Kevin is a software engineer at RStudio developing open source packages for big data analytics and machine learning. He has held data science positions across different industries, and has experience executing the end-to-end analytics process, from data engineering to model deployment and change management. Prior to RStudio, he was a principal data scientist at Honeywell, and also held roles at KPMG and Citi.

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