Object-oriented machine learning governance

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

2. The medium of claim 1 , wherein:

the operations execute quality management of modelling methods for implementation of machine learning design in an object-oriented modeling (OOM) framework.

3. The medium of claim 1 , wherein:

the modeled governance comprises a set of structures, processes, or policies by which pipeline development, deployment, or use functionality within an organization or set of organizations is directed, managed, or controlled.

4. The medium of claim 1 , wherein:

the modeled governance comprises a set of structures, processes, and policies by which pipeline development, deployment, and use functionality within an organization or set of organizations is directed, managed, and controlled.

5. The medium of claim 1 , wherein:

the modeled governance comprises a policy; and

the policy comprises a set of rules, controls, or resolutions put in place to dictate model behavior.

6. The medium of claim 1 , wherein:

the modeled governance comprises a policy; and

the policy comprises a set of rules, controls, or resolutions put in place to dictate model versioning.

7. The medium of claim 1 , wherein:

the modeled governance comprises a set of policies; and

meta-policies having detection rules are used to detect conflicts among the policies.

8. 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.

9. 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.

10. The medium of claim 9 , wherein:

a first subset of the plurality of object-orientation modelors are governance modelors;

the governance modelors are configured to form governance classes; and

a second subset of the plurality of object-manipulation functions are governance functions, wherein:

the governance functions are configured to leverage at least one of the governance classes.

11. The medium of claim 10 , wherein:

a first subset of the governance modelors are ontology governance modelors; and

a second subset of the governance modelors are taxonomy governance modelors.

12. The medium of claim 9 , wherein:

the governance attributes comprise a plurality of access levels for entity users of at least some of the plurality of entities; and

the specific targeted action comprises a plurality of sub-targets, wherein:

each of the plurality of sub-targets is assigned with a subset of the plurality of access levels.

13. The medium of claim 9 , wherein the plurality of object-manipulation functions comprise:

a sequence function used to change a collection of events into a time sequences for processing;

a feature function used to gather features of a first object-orientation modelor and then use the features in a second object-orientation modelor;

an economic function used to:

gather economic objectives and economic constraints of an entity; and

employ an allocation algorithm to maximize the objectives; and

an ensembling function used to combine a first subset of the library of classes.

14. The medium of claim 13 , wherein the plurality of object-manipulation functions are arranged to change orders dynamically based on the specific targeted action.

15. The medium of claim 9 , wherein the plurality of object-orientation modelors comprise:

ingestion modelors used to control schema drift of the datasets and add version numbers to the datasets;

landing modelors used to clean error records in the datasets and update the version numbers of the datasets;

curation modelors used to normalize the datasets, by adding primary surrogate keys, and update the version numbers of the datasets;

dimensional modelors used to encode the datasets in dimensional star schema and update the version numbers of the datasets; and

feature and label modelors used to:

change the datasets from dimensional star schema to denormalized flat table;

adjust granularity of the datasets; and

update the version numbers of the datasets.

16. The medium of claim 9 , wherein the datasets comprise:

training datasets, used to fit parameters of the object-orientation modelors;

validation datasets, used to tune the parameters of the object-orientation modelors;

quality assurance datasets, used to test accuracy of the object-orientation modelors;

association datasets, used to relate datasets to each other; and

targeted action datasets, used to determine the set of actions required to achieve the specific targeted action.

17. The medium of claim 9 , wherein:

each action from the set of actions is assigned with a score, the score indicating impact level of the action in achieving the specific targeted action.

18. The medium of claim 10 , wherein the object-orientated orchestration further comprises:

forming a first training dataset from the datasets;

training, with the one or more processors, a first machine-learning model on the first training dataset by adjusting parameters of the first machine-learning model to optimize a first objective function that indicates an accuracy of the governance functions in complying with the governance attributes; and

storing, with the one or more processors, the adjusted parameters of the trained first machine-learning model in memory.

19. The medium of claim 9 , wherein the object-orientated orchestration further comprises:

forming a first training dataset from the datasets;

training a first machine-learning model on the first training dataset by adjusting parameters of the first machine-learning model to optimize a first objective function that indicates an accuracy of the plurality of object-orientation modelors in complying with the governance attributes; and

storing the adjusted parameters of the trained first machine-learning model in memory.