1. A computer-implemented method, comprising: storing, at a server system, a set of previously trained predicative models; storing, at the server system, a respective performance indicator associated with each predictive model in the set of predictive models, where each of the respective performance indicators comprises a quantifiable metric determined based on prior usage data corresponding to the associated predictive model; receiving, at the server system, a first feature vector from a first remote computing device, the first feature vector comprising one or more elements; identifying, using the server system, an element type for each of the one or more elements of the first feature vector; selecting, using the server system, a first subset of predictive models from the set of predictive models, where the selection is based on the identified element types of the first feature vector and the stored performance indicators associated with the predictive models of the set; processing the first feature vector using the first subset of predictive models, each predictive model of the first subset of predictive models generating a respective predictive output based on the first feature vector to provide a first plurality of predictive outputs; generating a first combined predictive output based on the first plurality of predictive outputs; in response to generating the first combined predictive output, evaluating a performance of at least one predictive model of the subset; and updating the performance indicator associated with the at least one predictive model based on the evaluated performance.
1. A computer-implemented method, comprising: storing, at a server system, a set of previously trained predicative models; storing, at the server system, a respective performance indicator associated with each predictive model in the set of predictive models, where each of the respective performance indicators comprises a quantifiable metric determined based on prior usage data corresponding to the associated predictive model; receiving, at the server system, a first feature vector from a first remote computing device, the first feature vector comprising one or more elements; identifying, using the server system, an element type for each of the one or more elements of the first feature vector; selecting, using the server system, a first subset of predictive models from the set of predictive models, where the selection is based on the identified element types of the first feature vector and the stored performance indicators associated with the predictive models of the set; processing the first feature vector using the first subset of predictive models, each predictive model of the first subset of predictive models generating a respective predictive output based on the first feature vector to provide a first plurality of predictive outputs; generating a first combined predictive output based on the first plurality of predictive outputs; in response to generating the first combined predictive output, evaluating a performance of at least one predictive model of the subset; and updating the performance indicator associated with the at least one predictive model based on the evaluated performance. 12. The method of claim 1 , wherein processing the first feature vector comprises submitting the entire first feature vector to each predictive model of the first subset of predictive models.