University of South Florida
Machine learning (ML) techniques are becoming commonplace in business and research alike. With the automatization of data collection efforts, evermore data is being captured, rendering the task of extracting insightful patterns increasingly challenging. In addition to this "data avalanche" becoming ever more overwhelming, the usage of more computationally intensive algorithms in predictive analysis tasks also gives rise to new issues and challenges, so that a ML approach typically entails a trade o between computational efficiency and predictive performance. In recent years, however, new paradigms in analytics have been proposed geared towards solving these data and computational challenges, including cloud computing, distributed computing, and parallel computing approaches.