An electronic device comprising: at least one processor ; at least one memory storing telemetry data gathered from the electronic device , the telemetry data at least representing device usage data; an artificial intelligence (AI) client service that, in response to execution by the at least one processor , causes the at least one processor to: request from a cloud service at least one machine learning model based on the device metadata of the electronic device, receive the at least one machine learning models from the cloud service , the at least one machine learning model created for the electronic device based on the device metadata, update the received at least one machine learning models at the electronic device with information that is associated with at least one of the electronic device or a user of the electronic device, input the telemetry data from the memory into the updated at least one machine learning models , and determine, based on the telemetry data input into the updated at least one machine learning model, a notification to publish to a component of the electronic device that is customized to at least one of the user or the electronic device; and an AI client broker that, in response to execution by the at least one processor , publishes the determined notification to the component , the notification representing an event predicted to occur on the electronic device ; wherein the component configures the electronic device based at least on the published notifications. at least one processor ; at least one memory storing telemetry data gathered from the electronic device , the telemetry data at least representing device usage data; an artificial intelligence (AI) client service that, in response to execution by the at least one processor , causes the at least one processor to: request from a cloud service at least one machine learning model based on the device metadata of the electronic device, receive the at least one machine learning models from the cloud service , the at least one machine learning model created for the electronic device based on the device metadata, update the received at least one machine learning models at the electronic device with information that is associated with at least one of the electronic device or a user of the electronic device, input the telemetry data from the memory into the updated at least one machine learning models , and determine, based on the telemetry data input into the updated at least one machine learning model, a notification to publish to a component of the electronic device that is customized to at least one of the user or the electronic device; and request from a cloud service at least one machine learning model based on the device metadata of the electronic device, receive the at least one machine learning models from the cloud service , the at least one machine learning model created for the electronic device based on the device metadata, update the received at least one machine learning models at the electronic device with information that is associated with at least one of the electronic device or a user of the electronic device, input the telemetry data from the memory into the updated at least one machine learning models , and determine, based on the telemetry data input into the updated at least one machine learning model, a notification to publish to a component of the electronic device that is customized to at least one of the user or the electronic device; and an AI client broker that, in response to execution by the at least one processor , publishes the determined notification to the component , the notification representing an event predicted to occur on the electronic device ; wherein the component configures the electronic device based at least on the published notifications. <EOS>