10. A computer-implemented system comprising: one or more computers; one or more data storage devices coupled to the one or more computers, storing: a repository of training functions, and instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising: receiving training data for predictive modeling; setting an ultimate time by which each of a plurality of processes executing training functions obtained from the repository of training functions to generate respective trained predictive models is to be completed; executing the plurality of processes simultaneously in parallel to generate the trained predictive models using the training data; determining, after executing each of the plurality of processes for an initial time that is earlier than the ultimate time, a respective convergence status of each of the plurality of processes, wherein the convergence status indicates a likelihood that the process will converge, and based on the determination, identifying one or more processes that are not likely to converge, terminating processes that are not likely to converge, and allowing the remaining processes to continue executing; after the ultimate time has been reached, terminating processes that have not yet converged, and generating an effectiveness score for each of a plurality of trained predictive models that were generated by the remaining processes, wherein the effectiveness score for a particular trained predictive model represents an estimation of the effectiveness of the particular trained predictive model; storing the plurality of trained predictive models in a repository of trained predictive models; receiving input data and a prediction request; selecting, for use in servicing the prediction request, a first trained predictive model from among the plurality of trained predictive models based on their respective effectiveness scores; providing the input data to the first trained predictive model; and receiving a predictive output from the first trained predictive model.
10. A computer-implemented system comprising: one or more computers; one or more data storage devices coupled to the one or more computers, storing: a repository of training functions, and instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising: receiving training data for predictive modeling; setting an ultimate time by which each of a plurality of processes executing training functions obtained from the repository of training functions to generate respective trained predictive models is to be completed; executing the plurality of processes simultaneously in parallel to generate the trained predictive models using the training data; determining, after executing each of the plurality of processes for an initial time that is earlier than the ultimate time, a respective convergence status of each of the plurality of processes, wherein the convergence status indicates a likelihood that the process will converge, and based on the determination, identifying one or more processes that are not likely to converge, terminating processes that are not likely to converge, and allowing the remaining processes to continue executing; after the ultimate time has been reached, terminating processes that have not yet converged, and generating an effectiveness score for each of a plurality of trained predictive models that were generated by the remaining processes, wherein the effectiveness score for a particular trained predictive model represents an estimation of the effectiveness of the particular trained predictive model; storing the plurality of trained predictive models in a repository of trained predictive models; receiving input data and a prediction request; selecting, for use in servicing the prediction request, a first trained predictive model from among the plurality of trained predictive models based on their respective effectiveness scores; providing the input data to the first trained predictive model; and receiving a predictive output from the first trained predictive model. 18. The system of claim 10 , the operations further comprising: obtaining one or more updateable trained predictive models from a predictive model repository, wherein the received training data is different than initial training data that was used with one or more training functions to train the one or more updateable trained predictive models; and wherein executing the plurality of processes simultaneously in parallel to generate the plurality of trained predictive models using the training data and a plurality of training functions includes executing one or more processes to generate one or more retrained predictive models using the training data, the one or more updateable trained predictive models and the one or more training functions used to train said updateable trained predictive models.