公开(公告)号：US20220237505A1

公开(公告)日：2022-07-28

申请号：US17159639

申请日：2021-01-27

Applicant: salesforce.com, inc.

Inventor： Yuliya L. Feldman ,  Seyedshahin Ashrafzadeh ,  Alexandr Nikitin ,  Manoj Agarwal

IPC: G06N20/00 ,  G06F16/23

Abstract: Using container information to select containers for executing models is described. A system receives a request from an application and identifies a version of a machine-learning model associated with the request. The system identifies a set of each serving container corresponding to the machine-learning model from a cluster of available serving containers associated with the version of the machine-learning model. The system selects a serving container from the set of each serving container corresponding to the machine-learning model. If the machine-learning model is not loaded in the serving container, the system loads the machine-learning model in the serving container. If the machine-learning model is loaded in the serving container, the system executes, in the serving container, the machine-learning model on behalf of the request. The system responds to the request based on executing the machine-learning model on behalf of the request.

公开(公告)号：US11614932B2

公开(公告)日：2023-03-28

申请号：US17334617

申请日：2021-05-28

Applicant: Salesforce.com, Inc.

Inventor： Vaibhav Gumashta ,  Alexandr Nikitin ,  Yuliya L. Feldman ,  Seyedshahin Ashrafzadeh ,  Manoj Agarwal

IPC: G06F8/71 ,  G06N20/00 ,  G06F9/445

Abstract: Machine learning version management method for a prediction service includes receiving a prediction request, determining application metadata for the request that defines routing logic and a machine learning framework version, determining model metadata for the request that defines at least one model and at least one model version, forwarding the prediction request to the at least one model with the at least one model version, and returning a prediction from the at least one model to a requestor.

公开(公告)号：US20220414547A1

公开(公告)日：2022-12-29

申请号：US17357312

申请日：2021-06-24

Applicant: salesforce.com, inc.

Inventor： Seyedshahin Ashrafzadeh ,  Alexandr Nikitin ,  Vaibhav Gumashta ,  Yuliya L. Feldman ,  Manoj Agarwal ,  Swaminathan Sundaramurthy

IPC: G06N20/20 ,  G06K9/62

Abstract: Methods and systems for machine learning inferencing based on directed acyclic graphs are presented. A request for a machine learning application is received from a tenant application. A tenant identifier that identifies one of the tenants is determined from the request. Based on the tenant identifier and a type of the machine learning application, configuration parameters and a graph structure are determined. The graph structure defines a flow of operations for the machine learning application. Nodes of the graph structure are executed based on the configuration parameters to obtain a scoring result. Execution of a node causes a machine learning model generated for the first tenant to be applied to data related to the request. The scoring result is returned in response to the request.