A method for processing an event stream, comprising: receiving an event stream of events by a first plurality of first local modelers (110a - 110n) of a stream processing system, wherein each first local modeler (110a - 110n) receives a respective portion of the events, and wherein the first local modelers (110a - 110n) execute on a system of one or more computers; processing, by each first local modeler (110a - 110n) in parallel, events received by the first local modeler (110a - 110n), including aggregating information associated with each received event to generate aggregated information; providing, by one or more of the first local modelers (110a - 110n), to a first central modeler executing on the system of one or more computers, the aggregated information generated by the one or more first local modelers (110a - 110n); computing, by the first central modeler, an aggregated result over the entire stream of events using the received aggregated information, the aggregated result comprising statistics representing the received event stream and determining, by the first central modeler, parameters of a first machine learning model using the statistics representing the received event stream; the method further comprising: providing, by the first central modeler, one or more of the determined parameters of the first machine learning model to one or more first local modelers (110a - 110n) for storage; using the provided parameters to process, by the one or more first local modelers applying the first machine learning model, events received by the one or more first local modelers to perform scoring of the event stream and generate processed events. receiving an event stream of events by a first plurality of first local modelers (110a - 110n) of a stream processing system, wherein each first local modeler (110a - 110n) receives a respective portion of the events, and wherein the first local modelers (110a - 110n) execute on a system of one or more computers; processing, by each first local modeler (110a - 110n) in parallel, events received by the first local modeler (110a - 110n), including aggregating information associated with each received event to generate aggregated information; providing, by one or more of the first local modelers (110a - 110n), to a first central modeler executing on the system of one or more computers, the aggregated information generated by the one or more first local modelers (110a - 110n); computing, by the first central modeler, an aggregated result over the entire stream of events using the received aggregated information, the aggregated result comprising statistics representing the received event stream and determining, by the first central modeler, parameters of a first machine learning model using the statistics representing the received event stream; the method further comprising: providing, by the first central modeler, one or more of the determined parameters of the first machine learning model to one or more first local modelers (110a - 110n) for storage; using the provided parameters to process, by the one or more first local modelers applying the first machine learning model, events received by the one or more first local modelers to perform scoring of the event stream and generate processed events. providing, by the first central modeler, one or more of the determined parameters of the first machine learning model to one or more first local modelers (110a - 110n) for storage; using the provided parameters to process, by the one or more first local modelers applying the first machine learning model, events received by the one or more first local modelers to perform scoring of the event stream and generate processed events.