Scaling Distributed Machine Learning, to the Edge & Back
This talk will cover why and how organizations are distributing data storage and machine learning to the edge. By pushing machine learning to the edge, we can geographically distribute learning so that the models will actually learn different things relevant to specific locations. By delivering both edge database and compute in a single platform, more people can transition to a distributed architecture. The performance gains from this new architecture cements the value that mobile edge computing brings.