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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
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.
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公开(公告)号：US20220391747A1
公开(公告)日：2022-12-08
申请号：US17337387
申请日：2021-06-02
Applicant: salesforce.com, inc.
Inventor： Seyedshahin Ashrafzadeh , Yuliya L. Feldman , Alexandr Nikitin , Manoj Agarwal , Chirag Rajan , Swaminathan Sundaramurthy
IPC: G06N20/00
Abstract: A method by a router component in a multi-tenant on-demand serving infrastructure to route scoring requests to scoring containers. The method includes receiving a scoring request, determining a machine learning application associated with the scoring request, determining whether a router instance for the machine learning application exists, and responsive to a determination that a router instance for the machine learning application does not exist, obtaining a configuration object for the machine learning application and instantiating the router instance for the machine learning application based on the configuration object for the machine learning application. The method further includes invoking the router instance for the machine learning application to route the scoring request associated with the machine learning application to a scoring container that provides scoring functionality for the machine learning application.
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公开(公告)号：US20220391748A1
公开(公告)日：2022-12-08
申请号：US17337389
申请日：2021-06-02
Applicant: salesforce.com, inc.
Inventor： Alexandr Nikitin , Vaibhav Gumashta , Manoj Agarwal , Swaminathan Sundaramurthy
Abstract: A method of a base scorer in a scoring service container includes sending a model identifier to a model loader of an application specific scorer in the scoring service container, receiving a model object from the model loader in response to sending the model identifier, sending a request for a scoring from a client application to a scoring function of the application specific scorer, receiving the scoring from the application specific scorer, and returning the scoring to the client application.
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公开(公告)号：US20220391199A1
公开(公告)日：2022-12-08
申请号：US17337388
申请日：2021-06-02
Applicant: salesforce.com, inc.
Inventor： Seyedshahin Ashrafzadeh , Yuliya L. Feldman , Alexandr Nikitin , Manoj Agarwal , Chirag Rajan , Swaminathan Sundaramurthy
Abstract: A method by one or more electronic devices to provision an infrastructure for a machine learning application in a multi-tenant on-demand serving infrastructure. The method includes storing a plurality of templates, wherein each of the plurality of templates indicates a scoring interface, a web server, a definition of a continuous integration pipeline, and a definition of a continuous deployment pipeline, receiving a request to provision the infrastructure for the machine learning application using a specified template from the plurality of templates, and provisioning the infrastructure for the machine learning application using the specified template to create a version control system repository, a continuous integration pipeline, and a continuous deployment pipeline.
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公开(公告)号：US20220414548A1
公开(公告)日：2022-12-29
申请号：US17357419
申请日：2021-06-24
Applicant: salesforce.com, inc.
Inventor： Seyedshahin Ashrafzadeh , Alexandr Nikitin , Vaibhav Gumashta , Yuliya L. Feldman , Chirag Rajan , Manoj Agarwal , Swaminathan Sundaramurthy
Abstract: Methods and systems for multi-model scoring in a multi-tenant system are presented. A request for a machine learning application is received from a tenant application. A tenant identifier that identifies one of the multiple tenants is determined. Based on the tenant identifier and a type of the machine learning application, a first and a second machine learning models are determined. The first machine learning model was generated based on a first training data set associated with the tenant identifier. The second machine learning model that was generated based on a second training data set associated with the tenant identifier. A flow of operations that includes running the first and second machine learning models with data related to the request is executed to obtain a scoring result. The scoring result is returned to the tenant application in response to the request.
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6.发明申请
公开(公告)号：US20220391749A1
公开(公告)日：2022-12-08
申请号：US17337390
申请日：2021-06-02
Applicant: salesforce.com, inc.
Inventor： Yuliya L. Feldman , Seyedshahin Ashrafzadeh , Alexandr Nikitin , Chirag Rajan , Swaminathan Sundaramurthy
Abstract: A method performs service discovery in a machine learning service. The method includes detecting initialization of at least one service container, identifying label information in the at least one service container, collecting the label information for the initializing at least one service container, and storing the label information in a routing information storage to enable routing of requests to the at least one service container.
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公开(公告)号：US12204892B2
公开(公告)日：2025-01-21
申请号：US17337388
申请日：2021-06-02
Applicant: Salesforce.com, Inc.
Inventor： Seyedshahin Ashrafzadeh , Yuliya L Feldman , Alexandr Nikitin , Manoj Agarwal , Chirag Rajan , Swaminathan Sundaramurthy
IPC: G06F9/44 , G06F8/60 , G06F8/71 , G06F9/455 , G06F11/14 , G06F11/30 , G06F11/32 , G06F11/34 , G06N20/00 , G06F8/10 , G06F11/36
Abstract: A method by one or more electronic devices to provision an infrastructure for a machine learning application in a multi-tenant on-demand serving infrastructure. The method includes storing a plurality of templates, wherein each of the plurality of templates indicates a scoring interface, a web server, a definition of a continuous integration pipeline, and a definition of a continuous deployment pipeline, receiving a request to provision the infrastructure for the machine learning application using a specified template from the plurality of templates, and provisioning the infrastructure for the machine learning application using the specified template to create a version control system repository, a continuous integration pipeline, and a continuous deployment pipeline.
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公开(公告)号：US20220318647A1
公开(公告)日：2022-10-06
申请号：US17217406
申请日：2021-03-30
Applicant: salesforce.com, inc.
Inventor： Seyedshahin Ashrafzadeh , Yuliya Feldman , Manoj Agarwal , Chirag Rajan , Swaminathan Sundaramurthy , Endri Deliu
Abstract: A method and system for a single framework for both streaming and on-demand inference that includes receiving a request from a tenant application for a machine-learning serving infrastructure, where the request identifies features of tenant data and a machine-learning model, subscribing to events for the identified features, initiating the machine-learning model for the request, and generating a prediction using the machine-learning model on the identified features.
