1. A computer-implemented method comprising: maintaining, in a secondary memory of a computing system, a collection of trained predictive models, wherein each trained predictive model in the collection has a respective identifier; obtaining a plurality of records, wherein each record includes a time of a previously submitted predictive request and an identifier of a trained predictive model that provided a predictive output in response to the previously submitted predictive request, and wherein each of the plurality of records identifies the respective previously submitted predictive request as having been submitted by a first user; generating a trained scheduling model using the plurality of records as training data, wherein the trained scheduling model is a trained individual scheduling model that is specific to the first user; identifying, using the trained scheduling model, a particular set of trained predictive models that are most likely to receive predictive requests at a target time; selecting, for storing, one or more of the trained predictive models in the particular set of trained predictive models that have a likelihood of receiving a predictive request at the target time that is higher relative to others of the trained predictive models in the particular set of trained predictive models; and storing the selected one or more of the trained predictive models in the particular set of trained predictive models in a primary memory of the computing system, wherein the primary memory comprises volatile memory and the secondary memory comprises a non-volatile memory.