This document provides a summary of large scale machine learning frameworks. It discusses out-of-core learning, data parallelism using MapReduce, graph parallel frameworks like Pregel, and model parallelism using parameter servers. Spark is described as easy to use with a well-designed API, while GraphLab is designed for ML researchers with vertex programming. Parameter servers are presented as aiming to support very large learning but still being in early development.