One of the key advantages of using R for data mining and machine learning is that one may use the same environmentÊfor both data munging and algorithm execution. The problem with R, however, is that R's speed and memory constraints have limited the size of the datasets and the complexity of data.
Teradata's Aster R package was developed to lift these constraints by eliminating the requirement that the data be managed locally and by offering a large suite of multi-genre analytic functions that work via in-database execution. This session will examine a standard big data machine learning analytic workflow and present benchmarks from Teradata Aster R on several typical data cleaning and modeling tasks, showing how Teradata Aster R scales data science.
