Scaling up machine learning : parallel and distributed approaches This book presents an integrated collection of representative approaches for scaling up machine learning and data mining methods on parallel and distributed computing platforms. Demand for parallelizing learning algorithms is highly task specific: in some settings it is driven by the enormous dataset sizes in others by model complexity or by real time performance requirements. Making task appropriate algorithm and platform choices for large scale machine learning requires understanding the benefits trade offs and constraints of the available options