Top 11 Tools For Distributed Machine Learning

Centralised systems employ a strictly hierarchical approach. But the distributed system consists of a network of independent nodes and where no specific roles are assigned to certain nodes. A centralised solution is not the right choice when data is inherently distributed or too big to store on single machines. For instance, think about astronomical data that is too large to move and centralise. In a recent work published by the researchers at Delft University of Technology, Netherlands, they wrote in detail about the current state-of-the-art distributed ML models and how they affect computation latency and other attributes.