8. A computer-implemented method comprising: receiving, over a network, client training data comprising a first plurality of training data sets belonging to a client entity; generating a plurality of trained predictive models using a plurality of training functions and a first sample of the client training data; determining a respective accuracy of each of the plurality of trained predictive models using a different, second sample of the client training data; receiving, over the network one or more new training data sets belonging to the client entity, wherein each of the one or more new training data sets is new relative to the first plurality of training data sets; updating the client training data to include the one or more new training data sets; generating a plurality of new trained predictive models using the plurality of training functions and a different, third sample of the client training data; determining, a respective accuracy of each of the plurality of new trained predictive models using a different, fourth sample of the client training data; generating a respective effectiveness score for each of the plurality of trained predictive models and each of the plurality of new trained predictive models using the determined accuracy of its respective trained predictive model; receiving, over the network from a client computing system, a first prediction request and first input data; selecting a first trained predictive model to service the first prediction request from among the plurality of trained predictive models and the plurality of new trained predictive models based on the respective effectiveness scores; running the first trained predictive model on the first input data to generate a predictive output; and providing, to the client computing system, the predictive output in response to the first prediction request.
8. A computer-implemented method comprising: receiving, over a network, client training data comprising a first plurality of training data sets belonging to a client entity; generating a plurality of trained predictive models using a plurality of training functions and a first sample of the client training data; determining a respective accuracy of each of the plurality of trained predictive models using a different, second sample of the client training data; receiving, over the network one or more new training data sets belonging to the client entity, wherein each of the one or more new training data sets is new relative to the first plurality of training data sets; updating the client training data to include the one or more new training data sets; generating a plurality of new trained predictive models using the plurality of training functions and a different, third sample of the client training data; determining, a respective accuracy of each of the plurality of new trained predictive models using a different, fourth sample of the client training data; generating a respective effectiveness score for each of the plurality of trained predictive models and each of the plurality of new trained predictive models using the determined accuracy of its respective trained predictive model; receiving, over the network from a client computing system, a first prediction request and first input data; selecting a first trained predictive model to service the first prediction request from among the plurality of trained predictive models and the plurality of new trained predictive models based on the respective effectiveness scores; running the first trained predictive model on the first input data to generate a predictive output; and providing, to the client computing system, the predictive output in response to the first 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.