1. A system for optimizing a data model having a parameter set comprising a plurality of parameters, the system comprising: a master computer system controlling an iterative optimization process for optimizing the parameter set of the data model, wherein the master computer system is configured to perform operations comprising: dividing the parameter set of the data model into a plurality of parameter subsets; assigning each of the parameter subsets to a respective one of a plurality of worker computer systems, wherein each worker computer system is assigned a different parameter subset, and wherein no worker computer system is assigned the complete parameter set; receiving, from each of the worker computer systems, results of a respective evaluation of a closeness of fit, wherein each respective evaluation is an evaluation of a closeness of fit of classifications generated by the data model for a set of training data to known outputs for the set of training data, and wherein each respective evaluation is performed after the worker computer system has performed at least one sub-process of the iterative optimization process on the set of training data to determine updates for the values for the parameters in the parameter subset assigned to the worker computer system; determining an overall closeness of fit of the data model using the results of the respective evaluations of the closeness of fit received from each of the worker computer systems; and determining whether or not to perform another iteration of the iterative optimization process based on the overall closeness of fit of the data model.