1. A system, comprising: one or more computing devices configured to: receive, at a machine learning service of a provider network, an indication of a data source comprising observation records to be used to generate a model; identify one or more variables of the observation records as candidates for quantile binning transformations; determine a particular concurrent binning plan for at least a particular variable of the one or more variables, wherein, in accordance with the particular concurrent binning plan, a plurality of quantile binning transformations are applied to the particular variable during a training phase of the model, wherein the plurality of quantile binning transformations include a first quantile binning transformation with a first bin count and a second quantile binning transformation with a different bin count; generate, during the training phase, a parameter vector comprising respective initial weight values corresponding to a plurality of binned features obtained as a result of an implementation of the particular concurrent binning plan, including a first binned feature obtained using the first quantile binning transformation and a second binned feature obtained using the second quantile binning transformation; reduce, during the training phase, at least one weight value corresponding to a particular binned feature of the plurality of binned features in accordance with a selected optimization strategy; and obtain, during a post-training-phase prediction run of the model, a particular prediction using at least one of: the first binned feature or the second binned feature.