Speaker: Franz Kiraly, Alan Turing Institute
During the current data science boom, many companies and organizations that are not typical technology, internet, or Silicon Valley companies are beginning to scope the potential for applying rigorous quantitative methodology and machine learning.
This session describes what solutions companies want, how these companies may differ from the more “classical” consumers of machine learning and analytics, and the arising challenges that current and future high-performance computing (HPC) development may have to cope with. It includes stylized case examples from Franz Krialy's own work as a data analytics and machine learning consultant. Special focus will be given to the potentially large impact of parallelization and high-performance computing in modeling and specifically in checking whether a modeling strategy is sensible.