GENERATING AND MAINTAINING A FEATURE FAMILY REPOSITORY OF MACHINE LEARNING FEATURES
This disclosure describes a feature family system that, as part of an inter-network facilitation system, can intelligently generate and maintain a feature family repository for quickly and efficiently retrieving and providing machine learning features upon request. For example, the disclosed systems can generate a feature family repository as a centralized network location of feature references indicating network locations where different machine learning features are stored. In some cases, the disclosed systems identify a stored feature family that matches the request and retrieves the stored features from their respective network locations. The disclosed systems can generate feature families for online features as well as offline features and can automatically update feature values associated with various machine learning features on a period basis or in response to trigger events.
1 . A system comprising:
at least one processor; and
a non-transitory computer readable medium comprising instructions that, when executed by the at least one processor, cause the system to:
receive, from a first client device, an indication of a plurality of machine learning features to group within a feature family repository of an inter-network facilitation system;
in response to the indication of the plurality of machine learning features, generate a feature family to store within the feature family repository and comprising feature references indicating respective network locations where the plurality of machine learning features are stored;
receive a request to implement the feature family with a machine learning model; and
in response to the request, retrieve the plurality of machine learning features from the respective network locations indicated by the feature references of the feature family to provide the plurality of machine learning features to the machine learning model.
2 . The system of claim 1 , further comprising instructions that, when executed by the at least one processor, cause the system to receive the request to implement the feature family by receiving a request to verify an action associated with a second client device, the action comprising a login, a funds transfer, an account registration, a credit request, a transaction dispute, or an online payment.
3 . The system of claim 1 , further comprising instructions that, when executed by the at least one processor, cause the system to determine that the request indicates the feature family by comparing a stored entity name associated with the feature family within the feature family repository and a requested entity name indicated within the request.
4 . The system of claim 1 , further comprising instructions that, when executed by the at least one processor, cause the system to update feature values corresponding to each of the plurality of machine learning features within the feature family by requesting updated feature value data from the respective network locations on a periodic basis.
5 . The system of claim 1 , further comprising instructions that, when executed by the at least one processor, cause the system to provide the plurality of machine learning features to the machine learning model without generating a new feature family corresponding to the request.
6 . The system of claim 1 , further comprising instructions that, when executed by the at least one processor, cause the system to generate the feature family to include offline machine learning features stored at network locations updated on a periodic basis and online machine learning features stored at network locations updated concurrently with network activity within the inter-network facilitation system.
7 . The system of claim 1 , further comprising instructions that, when executed by the at least one processor, cause the system to receive the request to implement the feature family for one or more of training the machine learning model or applying the machine learning model for generating a prediction.
8 . A method comprising:
receiving, from a first client device, an indication of a plurality of machine learning features to group within a feature family repository of an inter-network facilitation system;
in response to the indication of the plurality of machine learning features, generating a feature family to store within the feature family repository and comprising feature references indicating respective network locations where the plurality of machine learning features are stored;
receiving a request to implement the feature family with a machine learning model; and
in response to the request, retrieving the plurality of machine learning features from the respective network locations indicated by the feature references of the feature family to provide the plurality of machine learning features to the machine learning model.
9 . The method of claim 8 , further comprising:
receiving an additional request to implement a different feature family not stored within the feature family repository; and
in response to the additional request, generating an additional feature family corresponding to the additional request to store within the feature family.
10 . The method of claim 8 , further comprising updating feature values associated with the plurality of machine learning features associated with the feature family based on detecting a trigger event associated with the plurality of machine learning features from network activity within the inter-network facilitation system.
11 . The method of claim 8 , further comprising:
identifying a machine learning feature associated with the feature family that is not required to perform an action associated with the request to implement the feature family; and
in response to identifying the machine learning feature that is not required, providing a modified subset of machine learning feature within the feature family to the machine learning model that does not include the machine learning feature that is not required.
12 . The method of claim 8 , further comprising generating, based on the plurality of machine learning features associated with the feature family indicated by the request and based on the machine learning model, predicted weights for the plurality of machine learning features for implementation via the machine learning model.
13 . The method of claim 8 , further comprising:
receiving a prediction query requesting feature information regarding the feature family used to generate a prediction via the machine learning model at a particular point in time; and
determining, in response to receiving the prediction query, feature values for the plurality of machine learning features associated with the feature family at the particular point in time.
14 . The method of claim 8 , further comprising:
determining feature names for machine learning features indicated within a plurality of requests for the feature family, wherein each of the plurality of requests indicate less than all stored feature names associated with the feature family;
identifying, based on comparing the feature names indicated within the plurality of requests with the stored feature names, a machine learning feature that is named in less than a threshold percent of the plurality of requests for the feature family; and
generating an additional feature family to exclude a feature reference indicating a network location where the machine learning feature that is named in less than a threshold percent of the plurality of requests is stored.
15 . A non-transitory computer readable medium comprising instructions that, when executed by at least one processor, cause a computing device to:
receive, from a first client device, an indication of a plurality of machine learning features to group within a feature family repository of an inter-network facilitation system;
in response to the indication of the plurality of machine learning features, generate a feature family to store within the feature family repository and comprising feature references indicating respective network locations where the plurality of machine learning features are stored;
receive a request to implement the feature family with a machine learning model; and
in response to the request, retrieve the plurality of machine learning features from the respective network locations indicated by the feature references of the feature family to provide the plurality of machine learning features to the machine learning model.
16 . The non-transitory computer readable medium of claim 15 , further comprising instructions that, when executed by the at least one processor, cause the computing device to determine an entity name associated with the request that indicates an entity corresponding to the feature family within the inter-network facilitation system.
17 . The non-transitory computer readable medium of claim 15 , further comprising instructions that, when executed by the at least one processor, cause the computing device to:
receive an additional request to implement a different feature family not stored within the feature family repository; and
determine similarity scores for a plurality of feature families stored within the feature family repository in relation to the different feature family indicated by the additional request.
18 . The non-transitory computer readable medium of claim 17 , further comprising instructions that, when executed by the at least one processor, cause the computing device to provide, in response to the additional request, one or more machine learning features indicated by a particular feature family with a highest similarity score in relation to the different feature family.
19 . The non-transitory computer readable medium of claim 17 , further comprising instructions that, when executed by the at least one processor, cause the computing device to determine the similarity scores by comparing machine learning features associated with the different feature family with machine learning features associated with each of the plurality of feature families stored within the feature family repository.
20 . The non-transitory computer readable medium of claim 15 , further comprising instructions that, when executed by the at least one processor, cause the computing device to:
receive an additional request to implement the feature family utilizing a different machine learning model; and
in response to the additional request, retrieve the plurality of machine learning features from the respective network locations to provide the plurality of machine learning features to the different machine learning model.