Machine Learning Requires Big Data Qubole

Last week, during the Deep Learning Summit at AWS re:Invent 2017, Terrence Sejnowski (a pioneer of deep learning) succinctly said "Whoever has more data wins". He was echoing a premise that has been repeated many times in many ways by many people: machine learning requires big data to work. Without large, well maintained training sets, machine learning algorithms--especially deep learning algorithms--fall far short of their potential. That's why here at Qubole we believe that enabling data scientists starts with giving them a platform to quickly select, clean, and aggregate datasets on a massive scale. The recent surge in impactful applications of deep learning algorithms has misled many people to believe that there has been a corresponding upswell in innovation in this field.