Machine learning pipeline optimization

Abstract

Provided is a process of modeling methods organized in racks of a machine learning pipeline to facilitate optimization of performance using modelling methods for implementation of machine learning design in an object-oriented modeling (OOM) framework, the process including: writing classes using object-oriented modelling of optimization methods, modelling methods, and modelling racks; writing parameters and hyper-parameters of the modeling methods as attributes as the modeling methods; scanning modelling racks classes to determine first class definition information; selecting a collection of rack and selecting modeling method objects; scanning modelling method classes to determine second class definition information; assigning racks and locations within the racks to modeling method objects; and invoking the class definition information to produce object manipulation functions that allow access the methods and attributes of at least some of the modeling method objects, the manipulation functions being configured to effectuate writing locations within racks and attributes of racks.

What is claimed is: 
     
         1 . A tangible, non-transitory, machine-readable medium storing instructions that when executed by one or more processors effectuate operations comprising:
 obtaining, with one or more processors, for a plurality of entities, datasets, wherein: 
 the datasets comprise a plurality of entity logs; 
 the entity logs comprise events involving the entities; 
 at least some of the events are actions by the entities; 
 at least some of the actions are targeted actions; and 
 the entity logs comprise or are otherwise associated with attributes of the entities, the attributes being distinct from the events; 
   orchestrating, with one or more processors, an object-orientated application or service by: 
 forming a plurality of objects, wherein each object of the plurality of objects comprises a set of attributes and events, at least some of the attributes and events different between the plurality of objects; 
 forming object-oriented labeled datasets based on the event and the attributes of each of the datasets; 
 forming a library or framework of classes with a plurality of object-orientation modelors; wherein forming the library or framework of classes comprises: 
 forming a first training dataset from the datasets; 
 training, with one or more processors, a first machine-learning modeling pipeline on the first training dataset by adjusting parameters of the first machine-learning modeling pipeline; and 
 forming the library or framework based on the adjusted parameters of the trained first machine-learning modeling pipeline; 
 
 forming a plurality of object-manipulation functions, each function being configured to leverage a respective class among the library or framework of classes; 
   receiving, with one or more processors, a request to determine a set of actions to increase likelihood of a given targeted action;   assigning, with one or more processors, the given targeted action to a first subset of classes from the library or framework of classes of the object-orientated application or service;   determining, with one or more processors, based on the assigning, the set of actions to increase likelihood of the given targeted action using a first subset of the plurality of object-manipulation functions leveraging the first subset of classes from the library or framework of classes of the object-orientated application or service; and   performing at least some of the set of actions to increase likelihood of the given targeted action.   
     
     
         2 . The medium of  claim 1 , wherein training comprises optimizing a first objective function that indicates an accuracy of the plurality of object-orientation modelors in generating the library or framework of classes. 
     
     
         3 . The medium of  claim 1 , wherein the orchestrating further comprises:
 adding version numbers to the datasets;   adding primary surrogate keys to the datasets and updating the version numbers; and   encoding the datasets in dimensional star schema and updating the version numbers.   
     
     
         4 . The medium of  claim 1 , wherein the orchestrating further comprises:
 storing, with one or more processors, the adjusted parameters of the trained first machine-learning modeling pipeline in memory.   
     
     
         5 . The medium of  claim 1 , wherein training comprises steps for training. 
     
     
         6 . The medium of  claim 1 , wherein the entities comprise consumers and the plurality of entity logs comprise:
 communications to consumers by an enterprise;   communications to an enterprise by consumers;   purchases by consumers from an enterprise; and   non-purchase interactions by consumers with an enterprise; and   wherein the entity logs are output of a customer relationship management system of an enterprise.   
     
     
         7 . The medium of  claim 6 , wherein:
 the enterprise is a credit card issuer and the given targeted action is predicting whether a consumer will default;   the enterprise is a lender and the given targeted action is predicting whether a consumer will borrow;   the enterprise is an insurance company and the given targeted action is predicting whether a consumer will file a claim;   the enterprise is an insurance company and the given targeted action is predicting whether a consumer will sign-up for insurance;   the enterprise is a vehicle seller and the given targeted action is predicting whether a consumer will purchase a vehicle;   the enterprise is a seller of goods and the given targeted action is predicting whether a consumer will file a warranty claim,   the enterprise is a wireless operator and the given targeted action is predicting whether a consumer upgrade their cellphone, or   the enterprise is a bank and the given targeted action is predicting gross domestic product (GDP) variation.   
     
     
         8 . The medium of  claim 1 , wherein the assignment of the given targeted action to a first subset of classes comprises:
 assigning the given targeted action to a first subset of the plurality of objects using a second subset of the plurality of object-manipulation functions; and   determining the first subset of classes from the library or framework of classes of the object-orientated application or service that are related to the first subset of the plurality of objects.   
     
     
         9 . The medium of  claim 1 , wherein the datasets comprise:
 a data frame;   a data stream;   a column in a table;   a row in a column;   a cell in a table;   structured data; and   unstructured data.   
     
     
         10 . The medium of  claim 1 , wherein the plurality of object-manipulation functions comprises:
 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 or framework of classes.   
     
     
         11 . The medium of  claim 10 , wherein the plurality of object-manipulation functions are arranged to perform in series. 
     
     
         12 . The medium of  claim 10 , wherein the plurality of object-manipulation functions are arranged to change orders dynamically based on the given targeted action. 
     
     
         13 . The medium of  claim 1 , wherein the plurality of object-orientation modelors comprises:
 a scaled propensity modelor used to calculate probability of a customer making an economic commitment;   a timing modelor used to calibrate moments in time when a customer is likely to engage with the given targeted action;   an affinity model or used to capture ranked likes and dislikes of an entity’s customers for a first subset of targeted actions;   a best action modelor used to create a framework for concurrent Key Performance Index of the given targeted action at different points in a customer’s journey; and   a cluster modelor used to group an entity’s customers based on the customers’ behavior into a finite list.   
     
     
         14 . The medium of  claim 13 , wherein the plurality of object-orientation modelors are arranged to perform at least one of in series, in parallel, and to change orders dynamically based on the given targeted action. 
     
     
         15 . The medium of  claim 1 , wherein:
 the given targeted action comprises a plurality of sub-targets; and   the plurality of object-orientation modelors comprises: 
 a scaled propensity modelor used to calculate probability of a customer making an economic commitment; 
 a timing modelor used to calibrate moments in time when a customer is likely to engage with each subset of the plurality of sub-targets; 
 an affinity modelor used to capture ranked likes and dislikes of an entity’s customers for a first subset of targeted actions; 
 a best action modelor used to create a framework for concurrent Key Performance Index for each subset of the plurality of sub-targets at different points in a customer’s journey; 
 a cluster modelor used to group an entity’s customers based on the customers’ behavior into a finite list; and 
 wherein: 
 a first subset of the plurality of object-orientation modelors are used for a first subset of the plurality of sub-targets; 
 a second subset of the plurality of object-orientation modelors are used for a second subset of the plurality of sub-targets; and 
 wherein the order in which the first subset of the plurality of object-orientation modelors perform is different from the order in which the second subset of the plurality of object-orientation modelors perform. 
 
   
     
     
         16 . The medium of  claim 1 , wherein the object-oriented labeled datasets formed according to an ontology of events. 
     
     
         17 . The medium of  claim 16 , wherein the ontology of events comprises Concurrent Ontology Labelling Datastore (COLD) methodology. 
     
     
         18 . The medium of  claim 1 , wherein the object-oriented labeled datasets formed according to a hierarchal taxonomy of events. 
     
     
         19 . The medium of  claim 1 , the operations comprising:
 steps for determining the set of actions required to achieve the given targeted action.   
     
     
         20 . A method, comprising:
 obtaining, with one or more processors, for a plurality of entities, datasets, wherein: 
 the datasets comprise a plurality of entity logs; 
 the entity logs comprise events involving the entities; 
 at least some of the events are actions by the entities; 
 at least some of the actions are targeted actions; and 
 the entity logs comprise or are otherwise associated with attributes of the entities, the attributes being distinct from the events; 
   orchestrating, with one or more processors, an object-orientated application or service by: 
 forming a plurality of objects, wherein each object of the plurality of objects comprises a set of attributes and events, at least some of the attributes and events different between the plurality of objects; 
 forming object-oriented labeled datasets based on the event and the attributes of each of the datasets, wherein: 
 the object-oriented labeled datasets formed according to an ontology of events; 
 wherein the ontology of events comprises Concurrent Ontology Labelling Datastore (COLD) methodology; and 
 wherein the object-oriented labeled datasets formed according to a hierarchal taxonomy of events; 
 
 forming a library or framework of classes with a plurality of object-orientation modelors, wherein the plurality of object-orientation modelors comprise: 
 a scaled propensity modelor used to calculate probability of a customer making an economic commitment; 
 a timing modelor used to calibrate moments in time when a customer is likely to engage with the given targeted action; 
 an affinity modelor used to capture ranked likes and dislikes of an entity’s customers for a first subset of targeted actions; 
 a best action modelor used to create a framework for concurrent Key Performance Index of the given targeted action at different points in a customer’s journey; and 
 a cluster modelor used to group an entity’s customers based on the customers’ behavior into a finite list; and 
 
 forming a plurality of object-manipulation functions, each function being configured to leverage a respective class among the library or framework of classes; 
   receiving, with one or more processors, a request to determine a set of actions to achieve, or increase likelihood of, a given targeted action;   assigning, with one or more processors, the given targeted action to a first subset of classes from the library or framework of classes of the object-orientated application or service; and   determining, with one or more processors, based on the assigning, the set of actions to achieve, or increase likelihood of, the given targeted action using a first subset of the plurality of object-manipulation functions leveraging the first subset of classes from the library or framework of classes of the object-orientated application or service.