This coming Tuesday, August 27, our US Chief Scientist Mario Inchosa will reveal some details of the forthcoming in-Hadoop predictive analytics capabilities of Revolution R Enterprise 7, due for release later this year. Here's the abtract of his webinar, High Performance Predictive Analytics in R and Hadoop:

Hadoop is rapidly being adopted as a major platform for storing and managing massive amounts of data, and for computing descriptive and query types of analytics on that data. However, it has a reputation for not being a suitable environment for high performance complex iterative algorithms such as logistic regression, generalized linear models, and decision trees.

At Revolution Analytics, we think that reputation is unjustified, and in this webinar, you will learn the approach we have taken to porting our suite of High Performance Analytics algorithms to run natively and efficiently in Hadoop. Our algorithms are written in C++ and R, and are based on a platform that automatically and efficiently parallelizes a broad class of algorithms called Parallel External Memory Algorithms (PEMA’s). This platform abstracts both the inter-process communication layer and the data source layer, so that the algorithms can work in almost any environment in which messages can be passed among processes and with almost any data source.

If want to learn how to use the computational resources of a Hadoop cluster to apply predictive models like regressions, decision trees, or clustering algorithms to data in Hadoop, register for the webinar at the link below.

Revolution Analytics Webinars: High Performance Predictive Analytics in R and Hadoop

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