Object-oriented machine learning governance

A tangible, non-transitory, machine-readable medium storing instructions that when executed by one or more processors in a computing system effectuate operations to manage compliance governance using modelling methods in a pipeline for implementation of machine learning design in an object-oriented modeling (OOM) framework, the operations comprising:

• forming, with the one or more processors, a first plurality of classes using object-oriented modelling of the modelling methods;
• forming, with the one or more processors, a second plurality of classes using object-oriented modelling of governance compliance methods;
• scanning, with the one or more processors, a class library containing modelling method classes to determine a first part of class definition information;
• scanning, with the one or more processors, a class library containing governance compliance classes to determine a second part of class definition information;
• and
• using, with the one or more processors, the first part of class definition information of the modeling method class and the second part of class definition information of the governance compliance classes to produce object manipulation functions that allow the computing system to access methods and attributes of a governance compliance object to manipulate a method class object.

A tangible, non-transitory, machine-readable medium storing instructions that when executed by one or more processors effectuate operations comprising:

• obtaining, with the one or more processors, for a plurality of entities, datasets, wherein:
• the datasets comprise a plurality of entity logs;
• a first subset of the plurality of entity logs are events involving the entities;
• a first subset of the events are actions by the entities;
• at least some of the actions are targeted actions;
• a second subset of the plurality of entity logs are attributes related to the entities;
• a first subset of the attributes are governance attributes, wherein the governance attributes comprise:
• entity restrictions for at least some of the plurality of entities;
• entity business protocols for at least some of the plurality of entities:
• entity policies for at least some of the plurality of entities;
• entity authorized users for at least some of the plurality of entities;
• and
• entity security protocols for at least some of the plurality of entities;
• and
• the events are distinct from the attributes;
• forming, with the one or more processors, an object-orientated orchestration, the object-orientated orchestration comprising:
• forming a plurality of objects, wherein each object of the plurality of objects comprises a different set of attributes and events;
• forming object-oriented labeled datasets based on the event and the attributes of each of the datasets;
• forming a library of classes, generated by a plurality of object-orientation modelors;
• and
• forming a plurality of object-manipulation functions, each function configured to leverage a specific class;
• receiving a request to determine a set of actions required to achieve a specific targeted action;
• assigning the specific targeted action to a first subset of classes from the library of classes of the object-oriented orchestration;
• and
• determining, with the one or more processors, the set of actions required to achieve the specific targeted action using a first subset of the plurality of object-manipulation functions related to the first subset of classes from the library of classes of the object-oriented orchestration.