Dask and Scikit-Learn -- Model Parallelism // Marginally Stable

This is the first of a series of posts discussing some recent experiments combining dask and scikit-learn. A small (and extremely alpha) library has been built up from these experiments, and can be found here. There are several ways of parallelizing algorithms in machine learning. Some algorithms can be made to be data-parallel (either across features or across samples). Many machine learning algorithms have hyperparameters which can be tuned to improve the performance of the resulting estimator.