8. A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations 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.
8. A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations 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. 14. The system of claim 8 , wherein generating the trained scheduling model comprises: training a scheduling model using, for each of the plurality of records, the respective time of the previously submitted request as input and the respective identifier of the trained predictive models that satisfied the request as output.