  Dramatic increases in the size and complexity of modern datasets have made
traditional "centralized" statistical inference prohibitive. In addition to
computational challenges associated with big data learning, the presence of
numerous data types (e.g. discrete, continuous, categorical, etc.) makes
automation and scalability difficult. A question of immediate concern is how to
design a data-intensive statistical inference architecture without changing the
basic statistical modeling principles developed for "small" data over the last
century. To address this problem, we present MetaLP, a flexible, distributed
statistical modeling framework.
