Visa International Service Association

Ranglin Lu

Yu Gu

Yinhe Cheng

Igor V Karpenko

Peter A Walker

Subir Roy

Described are a method, system, and computer program product for operating dynamic shadow testing environments for machine-learning models. The method includes generating a shadow testing environment operating at least two transaction services. The method also includes receiving a plurality of transaction authorization requests. The method further includes determining a first percentage associated with a first testing policy of the first transaction service and a second percentage associated with a second testing policy of the second transaction service. The method further includes replicating in the shadow testing environment, in real-time with processing the payment transactions, a first portion of the plurality of transaction authorization requests and a second portion of the plurality of transaction authorization requests. The method further includes testing the first transaction service using the first set of replicated transaction data and the second transaction service using the second set of replicated transaction data.

Method, system, and computer program product for operating dynamic shadow testing environments

A method, system, and computer program product for dynamically scheduling machine learning inference jobs receive or determine a plurality of performance profiles associated with a plurality of system resources, wherein each performance profile is associated with a machine learning model; receive a request for system resources for an inference job associated with the machine learning model; determine a system resource of the plurality of system resources for processing the inference job associated with the machine learning model based on the plurality of performance profiles and a quality of service requirement associated with the inference job; assign the system resource to the inference job for processing the inference job; receive result data associated with processing of the inference job with the system resource; and update based on the result data, a performance profile of the plurality of the performance profiles associated with the system resource and the machine learning model.

Method, system, and computer program product for dynamically scheduling machine learning inference jobs with different quality of services on a shared infrastructure

Described are a system, method, and computer program product for operating dynamic shadow testing environments for machine-learning models. The method includes storing a testing policy including an identifier of a machine-learning model and an identifier of a transaction service. The method includes generating a shadow testing environment operating the transaction service using the machine-learning model. The method also includes receiving, at a transaction service provider system, a transaction authorization request including transaction data of a transaction associated with a payment device. The method further includes identifying the machine-learning model associated with the transaction based on a parameter of the transaction data. The method further includes determining, based on the identifier of the machine-learning model, the testing policy and the shadow testing environment. The method further includes replicating the transaction data in the shadow testing environment as input for testing the transaction service using the machine-learning model.

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Austin, TX, United States of America

Ranglin Lu