Abstract: System derives training change factors for services provided for training product user, priority assigned to training service ticket initiated by training product user, times of service ticket interactions associated with training service ticket, and/or age of training service ticket, and also for times of states of training service ticket. System uses training service ticket and training change factors to train change-based machine-learning model to predict change-based training probability that training product user escalated service for training service ticket. System derives change factors for services provided for product user, priority assigned to service ticket initiated by product user, times of service ticket interactions associated with service ticket, and/or age of service ticket, and also for times of states of training service ticket.

Abstract: System derives training change factors for services provided for training product user, priority assigned to training service ticket initiated by training product user, times of service ticket interactions associated with training service ticket, and/or age of training service ticket, and also for times of states of training service ticket. System uses training service ticket and training change factors to train change-based machine-learning model to predict change-based training probability that training product user escalated service for training service ticket. System derives change factors for services provided for product user, priority assigned to service ticket initiated by product user, times of service ticket interactions associated with service ticket, and/or age of service ticket, and also for times of states of training service ticket.

Abstract: A computing device for interpreting a data model algorithm includes an object searcher, an interpreter, and a translator. The object searcher is configured to search for attributes within datasets generated from at least one method of an instantiation of the data model algorithm in a development mode workflow. The interpreter is configured to evaluate the attributes, identify attributes having a use type, identify the type information of the identified attribute, and create data schema using the identified attributes and type information. The use type can be determined based on attribute values or an interface type associated with an identified attribute. The translator is configured to compare the data schema with another data schema in response to selecting the data model algorithm for inclusion in a production mode workflow.

Abstract: A computing device for interpreting a data model algorithm includes an object searcher, an interpreter, and a translator. The object searcher is configured to search for attributes within datasets generated from at least one method of an instantiation of the data model algorithm in a development mode workflow. The interpreter is configured to evaluate the attributes, identify attributes having a use type, identify the type information of the identified attribute, and create data schema using the identified attributes and type information. The use type can be determined based on attribute values or an interface type associated with an identified attribute. The translator is configured to compare the data schema with another data schema in response to selecting the data model algorithm for inclusion in a production mode workflow.

Abstract: A computing device for interpreting a data model algorithm includes an object searcher, an interpreter, and a translator. The object searcher is configured to search for attributes within datasets generated from at least one method of an instantiation of the data model algorithm in a development mode workflow. The interpreter is configured to evaluate the attributes, identify attributes having a use type, identify the type information of the identified attribute, and create data schema using the identified attributes and type information. The use type can be determined based on attribute values or an interface type associated with an identified attribute. The translator is configured to compare the data schema with another data schema in response to selecting the data model algorithm for inclusion in a production mode workflow.

Abstract: System derives training change factors for services provided for training product user, priority assigned to training service ticket initiated by training product user, times of service ticket interactions associated with training service ticket, and/or age of training service ticket, and also for times of states of training service ticket. System uses training service ticket and training change factors to train change-based machine-learning model to predict change-based training probability that training product user escalated service for training service ticket. System derives change factors for services provided for product user, priority assigned to service ticket initiated by product user, times of service ticket interactions associated with service ticket, and/or age of service ticket, and also for times of states of training service ticket.

Abstract: A computing device for interpreting a data model algorithm includes an object searcher, an interpreter, and a translator. The object searcher is configured to search for attributes within datasets generated from at least one method of an instantiation of the data model algorithm in a development mode workflow. The interpreter is configured to evaluate the attributes, identify attributes having a use type, identify the type information of the identified attribute, and create data schema using the identified attributes and type information. The use type can be determined based on attribute values or an interface type associated with an identified attribute. The translator is configured to compare the data schema with another data schema in response to selecting the data model algorithm for inclusion in a production mode workflow.