Being able to parallelize `Predict` /`Classify` would be useful on multiple fronts:

 - Performance/Scale
 - Streaming
 - Pedagogical
 - Scientific

*Performance-wise* such parallelization would enable the use of larger datasets and HPC resources; *Streaming-wise* parallelization would enable certain real-time, distributed, cloud applications; *Pedgagogically-wise*, parallelization could enhance insights into the structure and judicious choice of predicting/classifying `Method`s while *Scientifically*, fine-grained, customized parallelization could promote reproducibility, training variations, extensions of built-in classifiers and the creation of new, hybrid methods. 


So are there good frameworks/strategies for parallelizing `Predict`/`Classify`?