Methods and system for providing simultaneous multi-task ensemble learning

A processor-readable storage medium storing processor-executable instructions, said instructions executable by a processor to:

• instantiate an automated data sampling and prediction structure training component,
• the automated data sampling and prediction structure training component being configured to automatically collect user event data samples, and use the collected user event data samples to train multiple prediction structures in parallel;
• receive, via the automated data sampling and prediction structure training component, user event data samples indicative of a user activity performance characteristic;
• identify user event data associated with one or more target attributes from the user event data samples;
• convert the user event data into a set of independent variables in compliance with a data format;
• retrieve a prediction structure that employs the set of independent variables as inputs and that generates a prediction objective output when the prediction structure is executed;
• validate the prediction structure with the set of independent variables when a validation metric triggers validation;
• and
• deploy, without exogenous interruption, the prediction structure upon validation.

A processor-readable storage medium storing processor-executable instructions, said instructions executable by a processor to:

• instantiate an automated data sampling and prediction structure training component,
• the automated data sampling and prediction structure training component being configured to automatically collect user event data samples, and use the collected user event data samples to train multiple prediction structures in parallel;
• retrieve a prediction structure (1) having a vector of variables as inputs and (2) that generates a prediction objective output during execution of the prediction structure;
• receive, via the automated data sampling and prediction structure training component, user event data samples related to the vector of variables;
• identify user event data associated with one or more target attributes from the user event data samples;
• convert the user event data into a set of independent variables in compliance with a format of the vector of variables;
• execute the prediction structure based on the set of independent variables to produce the prediction objective value;
• storing the prediction objective value with the user event data to produce updated user event data;
• incrementally update the prediction structure with the updated user event data and the prediction objective value to produce an updated prediction structure;
• and
• deploy, by the automated data sampling and prediction structure training component, the updated prediction structure upon incrementally updating.