9. The computer-implemented method of claim 8 , wherein the plurality of trained predictive models includes one or more trained updateable predictive models, and wherein the operations further comprise: generating a retrained updateable predictive model using a previously-trained updateable predictive model, the training function that was used to generate the previously-trained updateable predictive model, and a different, fifth, sample of the client training data; determining an accuracy of the retrained updateable predictive model using a different, sixth sample of data of the client training data; generating an effectiveness score for the retrained updateable predictive model using the determined accuracy of the retrained updateable predictive model; receiving a second prediction request and second input data; selecting a second trained predictive model to service the second prediction request from among the retrained updateable predictive model and the plurality of trained predictive models based on the respective effectiveness scores; and running the second trained predictive model on the second input data in response to the second prediction request.
9. The computer-implemented method of claim 8 , wherein the plurality of trained predictive models includes one or more trained updateable predictive models, and wherein the operations further comprise: generating a retrained updateable predictive model using a previously-trained updateable predictive model, the training function that was used to generate the previously-trained updateable predictive model, and a different, fifth, sample of the client training data; determining an accuracy of the retrained updateable predictive model using a different, sixth sample of data of the client training data; generating an effectiveness score for the retrained updateable predictive model using the determined accuracy of the retrained updateable predictive model; receiving a second prediction request and second input data; selecting a second trained predictive model to service the second prediction request from among the retrained updateable predictive model and the plurality of trained predictive models based on the respective effectiveness scores; and running the second trained predictive model on the second input data in response to the second prediction request. 10. The computer-implemented method of claim 9 , further comprising: determining, before generating the retrained updateable predictive model, that at least one of the following conditions is true: (i) an amount of training data in a training data queue is greater than or equal to a threshold amount; (ii) a predetermined amount of time is reached or exceeded; or (iii) a request to update the previously-trained updateable predictive model is received.