Amazon Technologies, Inc.

Stripe, Inc.

Vinayak Thapliyal

Ankit Kamboj

Aravind Ramarathinam

Santosh Chandrachood

Amit Purohit

Ramkumar Kamalapuram Sugavanam

Linchi Zhang

Min Zhou

Gayatri Ramesh Deo

Lukasz Misiuda

Chiranth Manjunath Hegde

One method includes identifying a set of fraud-detection ML models to be used to determine a fraud status of a transaction; executing a routing ML model configured to generate a score for at least a subset of the set of fraud-detection ML models, based on an input of an amount and an attribute associated with the transaction, the score indicative of the fraud status for the transaction based on the amount and the attribute; executing using the amount and the attribute associated with the transaction, a subset of the set of fraud-detection ML models selected in accordance with each fraud-detection ML model's respective score, whereby at least one fraud-detection ML model within the subset of the set of fraud-detection models generates a prediction associated with the fraud status of the transaction; and authorizing the transaction based on the prediction.

Artificial intelligence modeling for model routing without inference engines

In response to receiving a parallel processing job from a customer, a system operated by a computing resource service provider allocates and configures a cluster of computer systems capable of executing the job. In an embodiment, each computer system is configured with a first network stack that allows access to resources of the computing resource service provider and a second network stack that allows access to resources of the customer. In an embodiment, the state of the cluster is monitored by the system via the first network stack. In an embodiment, the system deploys a set of tasks on the cluster for fulfilling the processing job. In an embodiment, the tasks have access to the second network stack so that each task can access resources of the customer.

Cluster management

A system operated by a computing resource service provider maintains a pool of computing resources for use in processing job requests submitted by customers. The system allocates computing resources to the pool in anticipation of future resource needs. In various embodiments, future resource needs can be estimated based on scheduled jobs, or historical job information. In an embodiment, the computing resources are virtual computer systems which may be arranged in a cluster. In response to receiving a parallel processing job from a customer, the system reserves computing resources from the pool for performing the job. In an embodiment, the reserved resources are configured with a network namespace that is able to access to a customer's resources.

Resource management

Portions of distributed data processing programs may be dynamically allocated computing resources. When execution of a distributed data processing program is requested, interdependent portions of the resource may be dynamically computing resources according to requirements determined for the portions. A resource manager may, in some embodiments, respond to individual allocation requests for the different portions received from a program manager for the distributed data processing program. The allocated resources may be obtained from pools of computing resources that can be utilized by different distributed data processing programs.

Dynamically allocating resources for interdependent portions of distributed data processing programs

Growing community of inventors

Seattle, WA, United States of America

Vinayak Thapliyal