Cognitive Computing Architectures for Machine (Deep) Learning at Scale

The paper reviews existing models for organizing information for machine learning systems in heterogeneous computing environments. In this context, we focus on structured knowledge representations as they have played a key role in enabling machine learning at scale. The paper highlights recent case studies where knowledge structures when combined with the knowledge of the distributed computation graph have accelerated machine-learning applications by 10x or more. We extend these concepts to the design of Cognitive Distributed Learning Systems to resolve critical bottlenecks in real-time machine learning applications such as Predictive Analytics and Recommender Systems.