What's the difference between Spark ML and MLLIB packages -

i noticed there 2 linearregressionmodel classes in sparkml, 1 in ml , 1 in mllib package.

these 2 implemented quite differently - e.g. 1 mllib implements serializable, while other 1 not.

by way ame true randomforestmodel.

why there 2 classes? "right" one? , there way convert 1 another?

o.a.s.mllib contains old rdd-based api while o.a.s.ml contains new api build around dataset , ml pipelines. ml , mllib reached feature parity in 2.0.0 , mllib being deprecated (this happened in case of linear regression) , removed in next major release.

so unless goal backwards compatibility "right choice" o.a.s.ml.