Motivation for this function is the fact that some models - e.g., very
complex machine learning models fit to large datasets - may take a long
time to complete. Splitting the model creation request from model retrieval
in these cases allows the user to perform other interactive R session tasks
between the time the model creation/update request is made and the time the
final model is available.
Either `sample_pct` or `training_row_count` can be used to specify the amount of data to
use, but not both. If neither are specified, a default of the maximum amount of data that
can safely be used to train any blueprint without going into the validation data will be
selected.
In smart-sampled projects, `samplePct` and `trainingRowCount` are assumed to be in terms of rows
of the minority class.