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. <EOS>