Dask in machine learning originally consisted of a number of smaller libraries focused around particular sub-domains of machine learning.
- dask-searchcv: Scalable model selection
- dask-glm: Generalized Linear Model solvers
- dask-xgboost: Connection to the XGBoost library
- dask-tensorflow: Connection to the Tensorflow library
While these special-purpose libraries were convenient for development, they
were inconvenient for users who found the number of libraries daunting. The
dask-ml project started as a combination of these that presented a single
unified API and entry-point that mimicked Scikit-Learn. Afterwards additional
algorithm development happened in the
dask-ml library itself.
The pre-existing libraries are still valid and dask-ml defers to them for future development.