15. A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform actions including: accessing a set of action messages, each action message of the set of action messages including a set of key/value pairs and an identification of an action assigned to the action message by an abstraction layer, the action representing an actual or inferred user interaction with at least part of a website; extracting, from an action message in the set of action messages, a website identifier, the action message being associated with a user device; determining a session identifier for the action message; identifying, based on the website identifier, a current predictive model configured to generate action predictions, the current predictive model being configured to process a representation one or more past action observations to estimate one or more hidden variables and to generate output data corresponding to a subsequent-action prediction based on the one or more hidden variables; processing input data representing the action identified in the action message using the current predictive model to estimate the one or more hidden variables and to generate the output data corresponding to the subsequent-action prediction; generating an enhanced action message for the action message, at least part of the enhanced action message including prediction information corresponding to the output data, the prediction information representing a likelihood of subsequently detecting, in association with the user device, one or more user interactions with the website that correspond to a particular type of event or action; and outputting the enhanced action message to an enhanced-action-message sink, the enhanced-action-message sink including one or both of a downstream system and a remote system.
15. A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform actions including: accessing a set of action messages, each action message of the set of action messages including a set of key/value pairs and an identification of an action assigned to the action message by an abstraction layer, the action representing an actual or inferred user interaction with at least part of a website; extracting, from an action message in the set of action messages, a website identifier, the action message being associated with a user device; determining a session identifier for the action message; identifying, based on the website identifier, a current predictive model configured to generate action predictions, the current predictive model being configured to process a representation one or more past action observations to estimate one or more hidden variables and to generate output data corresponding to a subsequent-action prediction based on the one or more hidden variables; processing input data representing the action identified in the action message using the current predictive model to estimate the one or more hidden variables and to generate the output data corresponding to the subsequent-action prediction; generating an enhanced action message for the action message, at least part of the enhanced action message including prediction information corresponding to the output data, the prediction information representing a likelihood of subsequently detecting, in association with the user device, one or more user interactions with the website that correspond to a particular type of event or action; and outputting the enhanced action message to an enhanced-action-message sink, the enhanced-action-message sink including one or both of a downstream system and a remote system. 20. The computer-program product as recited in claim 15 , wherein the prediction information identifies a probability of a particular type of interaction occurring during a session, the session being associated with the session identifier, and wherein the actions further include: generating a Receiver Operating Characteristic curve based on a set of previously generated output data and corresponding action data; and determining a value of a confidence metric based on the Receiver Operating Characteristic curve and the probability.