EP-4736072-A2 - SYSTEM, METHOD, AND COMPUTER PROGRAM PRODUCT FOR INCREMENTAL LEARNING

Abstract

Systems, methods, and computer program products for incremental learning are provided. A system includes at least one processor programmed or configured to execute a first machine-learning model for each input of a plurality of inputs associated with a plurality of requests in a production environment, determine to train the first machine-learning model based on at least one rule, in response to determining to train the first machine-learning model, creating a second machine-learning model including weights from the first machine-learning model, train the second-machine learning model with the model data stored in the at least one data storage device, determine whether to replace the first machine-learning model with the second machine-learning model, in response to determining to replace the first machine-learning model with the second machine-learning model, and replace the first machine-learning model with the second machine-learning model in the production environment.

Inventors

• HE, Runxin
• LOU, MINGJI
• KERSTING, Nicholas, Stephen
• LI, Songshan
• HO, Iat, Kei
• OKOCHU, Raphael
• GU, YU

Assignees

• Visa International Service Association

Dates

Publication Date

20260506

Application Date

20240627

Claims (20)

1. 1 . A system comprising: at least one data storage device; and at least one processor programmed or configured to: execute a first machine-learning model for each input of a plurality of inputs associated with a plurality of requests in a production environment, the first machine-learning model configured to output an inference for each input; store, in the at least one data storage device, model data for each execution of the first machine-learning model; determine to train the first machine-learning model based on at least one rule; in response to determining to train the first machine-learning model, creating a second machine-learning model comprising weights from the first machine-learning model; train the second machine-learning model with the model data stored in the at least one data storage device; determine whether to replace the first machine-learning model with the second machine-learning model; and in response to determining to replace the first machine-learning model with the second machine-learning model, replace the first machinelearning model with the second machine-learning model in the production environment such that a next plurality of requests are input to the second machine-learning model.
2. 2. The system of claim 1 , where the plurality of requests are processed as a batch by executing the first machine-learning model for each input of the plurality of inputs associated with the plurality of requests.
3. 3. The system of claim 2, wherein the plurality of requests are processed by a batch processor, and wherein the at least one processor is further programmed or configured to generate a dashboard interface configured to communicate with the batch processor.
4. 4. The system of claim 1 , wherein determining whether to replace the first machine-learning model with the second machine-learning model comprises comparing the first machine-learning model to the second machine-learning model.
5. 5. The system of claim 4, wherein determining whether to replace the first machine-learning model with the second machine-learning model is based on at least one of computation efficiency and accuracy.
6. 6. The system of claim 1 , wherein the at least one rule is based on at least one of model score and feature distribution.
7. 7. The system of claim 1 , wherein the inference comprises a score.
8. 8. A computer-implemented method comprising: executing, with at least one processor, a first machine-learning model for each input of a plurality of inputs associated with a plurality of requests in a production environment, the first machine-learning model configured to output an inference for each input; storing, in at least one data storage device, model data for each execution of the first machine-learning model; determining, with at least one processor, to train the first machinelearning model based on at least one rule; in response to determining to train the first machine-learning model, creating a second machine-learning model comprising weights from the first machinelearning model; training, with at least one processor, the second machine-learning model with the model data stored in the at least one data storage device; determining, with at least one processor, whether to replace the first machine-learning model with the second machine-learning model; and in response to determining to replace the first machine-learning model with the second machine-learning model, replacing the first machine-learning model with the second machine-learning model in the production environment such that a next plurality of requests are input to the second machine-learning model.
9. 9. The method of claim 8, where the plurality of requests are processed as a batch by executing the first machine-learning model for each input of the plurality of inputs associated with the plurality of requests.
10. 10. The method of claim 9, wherein the plurality of requests are processed by a batch processor, further comprising generating a dashboard interface configured to communicate with the batch processor.
11. 1 1 . The method of claim 8, wherein determining whether to replace the first machine-learning model with the second machine-learning model comprises comparing the first machine-learning model to the second machine-learning model.
12. 12. The method of claim 1 1 , wherein determining whether to replace the first machine-learning model with the second machine-learning model is based on at least one of computation efficiency and accuracy.
13. 13. The method of claim 8, wherein the at least one rule is based on at least one of model score and feature distribution.
14. 14. The method of claim 8, wherein the inference comprises a score.
15. 15. A computer program product comprising at least one non- transitory computer-readable medium including program instructions that, when executed by at least one processor, causes the at least one processor to: execute a first machine-learning model for each input of a plurality of inputs associated with a plurality of requests in a production environment, the first machine-learning model configured to output an inference for each input; store, in at least one data storage device, model data for each execution of the first machine-learning model; determine to train the first machine-learning model based on at least one rule; in response to determining to train the first machine-learning model, create a second machine-learning model comprising weights from the first machinelearning model; train the second machine-learning model with the model data stored in the at least one data storage device; determine whether to replace the first machine-learning model with the second machine-learning model; and in response to determining to replace the first machine-learning model with the second machine-learning model, replace the first machine-learning model with the second machine-learning model in the production environment such that a next plurality of requests are input to the second machine-learning model.
16. 16. The computer program product of claim 15, where the plurality of requests are processed as a batch by executing the first machine-learning model for each input of the plurality of inputs associated with the plurality of requests.
17. 17. The computer program product of claim 16, wherein the plurality of requests are processed by a batch processor, and wherein the at least one processor is further programmed or configured to generate a dashboard interface configured to communicate with the batch processor.
18. 18. The computer program product of claim 15, wherein determining whether to replace the first machine-learning model with the second machine-learning model comprises comparing the first machine-learning model to the second machinelearning model.
19. 19. The computer program product of claim 18, wherein determining whether to replace the first machine-learning model with the second machine-learning model is based on at least one of computation efficiency and accuracy.
20. 20. The computer program product of claim 15, wherein the at least one rule is based on at least one of model score and feature distribution.

Description

SYSTEM, METHOD, AND COMPUTER PROGRAM PRODUCT FOR INCREMENTAL LEARNING CROSS REFERENCE TO RELATED APPLICATION [0001] This application claims the benefit of U.S. Provisional Patent Application No. 63/523,956, filed on June 29, 2023, the disclosure of which is hereby incorporated by reference in its entirety. BACKGROUND 1. Field [0002] This disclosure relates generally to machine-learning and, in some nonlimiting embodiments or aspects, to systems, methods, and computer program products for incremental learning for a model in a production environment. 2. Technical Considerations [0003] Inference platforms to monitor a machine-learning model in production have several technical limitations. For example, training is a separate process and retraining the model may take a considerable amount of resources (e.g., memory, processing, time, etc.). The production and training environments both are associated with workloads and overheads. Further, during training of such models there are limited opportunities for production tests. Moreover, the training process is often slow in response to data distribution during the in-production generation of inferences. SUMMARY [0004] According to non-limiting embodiments or aspects, provided is a system comprising: at least one data storage device; and at least one processor programmed or configured to: execute a first machine-learning model for each input of a plurality of inputs associated with a plurality of requests in a production environment, the first- machine learning model configured to output an inference for each input; store, in the at least one data storage device, model data for each execution of the first machinelearning model; determine to train the first machine-learning model based on at least one rule; in response to determining to train the first machine-learning model, creating a second machine-learning model comprising weights from the first machine-learning model; train the second-machine learning model with the model data stored in the at least one data storage device; determine whether to replace the first machine-learning model with the second machine-learning model; and in response to determining to replace the first machine-learning model with the second machine-learning model, replace the first machine-learning model with the second machine-learning model in the production environment such that a next plurality of requests are input to the second machine-learning model. In non-limiting embodiments or aspects, where the plurality of requests are processed as a batch by executing the first machine-learning model for each input of the plurality of inputs associated with the plurality of requests. [0005] In non-limiting embodiments or aspects, the plurality of requests are processed by a batch processor, and the at least one processor is further programmed or configured to generate a dashboard interface configured to communicate with the batch processor. In non-limiting embodiments or aspects, determining whether to replace the first machine-learning model with the second machine-learning model comprises comparing the first machine-learning model to the second machine-learning model. In non-limiting embodiments or aspects, determining whether to replace the first machine-learning model with the second machine-learning model is based on at least one of computation efficiency and accuracy. In non-limiting embodiments or aspects, the at least one rule is based on at least one of model score and feature distribution. In non-limiting embodiments or aspects, the inference comprises a score. [0006] According to non-limiting embodiments or aspects, provided is a computer- implemented method comprising: executing, with at least one processor, a first machine-learning model for each input of a plurality of inputs associated with a plurality of requests in a production environment, the first-machine learning model configured to output an inference for each input; storing, in at least one data storage device, model data for each execution of the first machine-learning model; determining, with at least one processor, to train the first machine-learning model based on at least one rule; in response to determining to train the first machine-learning model, creating a second machine-learning model comprising weights from the first machine-learning model; training, with at least one processor, the second-machine learning model with the model data stored in the at least one data storage device; determining, with at least one processor, whether to replace the first machine-learning model with the second machine-learning model; and in response to determining to replace the first machinelearning model with the second machine-learning model, replacing the first machinelearning model with the second machine-learning model in the production environment such that a next plurality of requests are input to the second machine-learning model. [0007] In non-limiting embodiments or asp