Object-oriented machine learning governance

Abstract

Provided is a process including: writing, with a computing system, a first plurality of classes using object-oriented modelling of modelling methods; writing, with the computing system, a second plurality of classes using object-oriented modelling of governance; scanning, with the computing system, a set of libraries collectively containing both modelling object classes among the first plurality of classes and governance classes among the second plurality of classes to determine class definition information; using, with the computing system, at least some of the class definition information to produce object manipulation functions, wherein the object manipulation functions allow a governance system to access methods and attributes of classes among first plurality of classes or the second plurality of classes to manipulate objects of at least some of the modelling object classes; and using at least some of the class definition information to effectuate access to the object manipulation functions.

What is claimed is: 
     
         1 . A tangible, non-transitory, machine-readable medium storing instructions that when executed by one or more processors in a computing system effectuate operations to measure performance of processing of data constructs in a pipeline using modelling methods for implementation of machine learning design in an object-oriented modeling (OOM) framework, the operations comprising:
 writing, with the computing system, a first plurality of classes using object-oriented modelling of the modelling methods;   writing, with the computing system, a second plurality of classes using object-oriented modelling of quality measurement methods;   scanning, with the computing system, a class library containing modelling method classes to determine a first part of class definition information;   scanning, with the computing system, another class library containing quality management classes to determine a second part of class definition information; and   invoking, with the computing system, the class definition information to produce object manipulation functions that allow the computing system to access methods and attributes of data classes to manipulate a modeling method class.   
     
     
         2 . The medium of  claim 1 , wherein:
 quality monitoring (MQM), score quality monitoring (SQM), bias quality management (BQM), privacy quality management (PQM), or label quality monitoring (LQM).   
     
     
         3 . The medium of  claim 1 , wherein:
 the operations comprise object manipulation by allowing reading of attributes, usage of a given modeling method, audit of usage of a given modeling object, reporting attempts to use the given modeling object, or verifying proper licensing.   
     
     
         4 . The medium of  claim 1 , wherein:
 the operations comprise object manipulation by allowing reading of attributes, usage of a given modeling method, audit of usage of a given modeling object, reporting attempts to use the given modeling object, and verifying proper licensing; and   the quality measurement methods comprise data quality monitoring (DQM), model quality monitoring (MQM), score quality monitoring (SQM), bias quality management (BQM), privacy quality management (PQM), and label quality monitoring (LQM).   
     
     
         5 . The medium  claim 1 , wherein:
 the operations further comprise processing data construct objects based on entity logs;   entities captured in the data construct objects comprise:
 consumers, 
 communications to consumers by an enterprise, 
 communications to an enterprise by consumers, and 
 events that include purchases by consumers from the enterprise and non-purchase interactions by consumers with the enterprise; and 
   the entity logs are obtained from a customer relationship management system of the enterprise.   
     
     
         6 . The medium  claim 1 , wherein:
 the enterprise is a credit card issuer and a trained predictive machine learning model developed using the object-oriented modeling (OOM) framework is configured to predict whether a consumer will default;   the enterprise is a lender and the trained predictive machine learning model developed using the OOM framework is configured to predict whether a consumer will borrow;   the enterprise is an insurance company and the trained predictive machine learning model developed using the OOM framework is configured to predict whether a consumer will file a claim;   the enterprise is an insurance company and the trained predictive machine learning model developed using the OOM framework is configured to predict whether a consumer will sign-up for insurance;   the enterprise is a vehicle seller and the trained predictive machine learning model developed using the OOM framework is configured to predict whether a consumer will purchase a vehicle;   the enterprise is a seller of goods and the trained predictive machine learning model developed using the OOM framework is configured to predict whether a consumer will file a warranty claim;   the enterprise is a wireless operator and the trained predictive machine learning model developed using the OOM framework is configured to predict whether a consumer upgrade their cellphone; or   the enterprise is bank and the trained predictive machine learning model developed using the OOM framework is configured to predict the change in GDP.   
     
     
         7 . The medium of  claim 1 , wherein:
 the operations comprise steps for object-oriented orchestration.   
     
     
         8 . The medium of  claim 1 , wherein:
 the operations comprise steps for scaled propensity modeling.   
     
     
         9 . The medium of  claim 1 , wherein:
 the operations comprise steps for Object-Oriented Modeling transformation from data to labeled data.   
     
     
         10 . The medium of  claim 1 , wherein:
 the operations comprise steps for Object-Oriented Modeling composition of object-oriented pillars.   
     
     
         11 . A method comprising:
 writing, with a computing system, a first plurality of classes using object-oriented modelling of modelling methods;   writing, with the computing system, a second plurality of classes using object-oriented modelling of quality measurement methods;   scanning, with the computing system, a class library containing modelling method classes to determine a first part of class definition information;   scanning, with the computing system, another class library containing quality management classes to determine a second part of class definition information; and   invoking, with the computing system, the class definition information to produce object manipulation functions that allow the computing system to access methods and attributes of data classes to manipulate a modeling method class.   
     
     
         12 . The method of  claim 11 , wherein:
 quality monitoring (MQM), score quality monitoring (SQM), bias quality management (BQM), privacy quality management (PQM), or label quality monitoring (LQM).   
     
     
         13 . The method of  claim 11 , wherein:
 the method comprises object manipulation by allowing reading of attributes, usage of a given modeling method, audit of usage of a given modeling object, reporting attempts to use the given modeling object, or verifying proper licensing.   
     
     
         14 . The method of  claim 11 , wherein:
 the method comprises object manipulation by allowing reading of attributes, usage of a given modeling method, audit of usage of a given modeling object, reporting attempts to use the given modeling object, and verifying proper licensing; and   the quality measurement methods comprise data quality monitoring (DQM), model quality monitoring (MQM), score quality monitoring (SQM), bias quality management (BQM), privacy quality management (PQM), and label quality monitoring (LQM).   
     
     
         15 . The method  claim 11 , wherein:
 the method further comprises processing data construct objects based on entity logs;   entities captured in the data construct objects comprise:
 consumers, 
 communications to consumers by an enterprise, 
 communications to an enterprise by consumers, and 
 events that include purchases by consumers from the enterprise and non-purchase interactions by consumers with the enterprise; and 
   the entity logs are obtained from a customer relationship management system of the enterprise.   
     
     
         16 . The method  claim 11 , wherein:
 the enterprise is a credit card issuer and a trained predictive machine learning model developed using the object-oriented modeling (OOM) framework is configured to predict whether a consumer will default;   the enterprise is a lender and the trained predictive machine learning model developed using the OOM framework is configured to predict whether a consumer will borrow;   the enterprise is an insurance company and the trained predictive machine learning model developed using the OOM framework is configured to predict whether a consumer will file a claim;   the enterprise is an insurance company and the trained predictive machine learning model developed using the OOM framework is configured to predict whether a consumer will sign-up for insurance;   the enterprise is a vehicle seller and the trained predictive machine learning model developed using the OOM framework is configured to predict whether a consumer will purchase a vehicle;   the enterprise is a seller of goods and the trained predictive machine learning model developed using the OOM framework is configured to predict whether a consumer will file a warranty claim;   the enterprise is a wireless operator and the trained predictive machine learning model developed using the OOM framework is configured to predict whether a consumer upgrade their cellphone; or   the enterprise is bank and the trained predictive machine learning model developed using the OOM framework is configured to predict the change in GDP.   
     
     
         17 . The method of  claim 11 , comprising steps for object-oriented orchestration. 
     
     
         18 . The method of  claim 11 , comprising steps for scaled propensity modeling. 
     
     
         19 . The method of  claim 11 , comprising steps for Object-Oriented Modeling transformation from data to labeled data. 
     
     
         20 . The method of  claim 11 , comprising steps for Object-Oriented Modeling composition of object-oriented pillars.