Machine Learning Infrastructures Require Scale to Spare

Used across a wide range of processes to improve or replace human input, machine learning is attractive because it is able to addresses problems never before tackled due to the prohibitively large volumes of data involved. But when it comes to managing these often massive data sets, it's important to think big from the start to ensure long term success. In fact, the larger the data set, the more potential value that machine learning brings. As an example, at the University of Miami's Center for Computational Sciences (CCS) scientists are working with the city of Miami on a machine-learning project designed to improve service and maintenance for Miami's famous Beach Walk to drive better strategies for services and maintenance schedules, to provide real-time insights that can help improve public safety and service responses. The Beach Walk has a 30-block radius with a variety of terrain and hundreds of ingress points, so the project is using sound and light as a proxy for gathering "people-movement" data.