公开(公告)号：US20220300259A1

公开(公告)日：2022-09-22

申请号：US17835121

申请日：2022-06-08

Applicant: AT&T Intellectual Property I, L.P.

Inventor： Jeffrey B. Saxon ,  Jeffrey Dix

IPC: G06F8/35 ,  G06N20/00 ,  G06F8/61 ,  H04L67/00

Abstract: Aspects of the subject disclosure may include, for example, a device, including a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations including receiving user specified metadata for execution tasks associated with a machine learning (ML) model; receiving artifacts specifying program code for implementing the ML model; creating a file system structure for a container to hold the ML model; receiving environment variables for operation of the ML model; and building the container including a model image for the ML model. Other embodiments are disclosed.

公开(公告)号：US11620113B2

公开(公告)日：2023-04-04

申请号：US17835121

申请日：2022-06-08

Applicant: AT&T Intellectual Property I, L.P.

Inventor： Jeffrey B. Saxon ,  Jeffrey Dix

IPC: G06F8/35 ,  G06F8/61 ,  G06N20/00 ,  H04L67/00

Abstract: Aspects of the subject disclosure may include, for example, a device, including a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations including receiving user specified metadata for execution tasks associated with a machine learning (ML) model; receiving artifacts specifying program code for implementing the ML model; creating a file system structure for a container to hold the ML model; receiving environment variables for operation of the ML model; and building the container including a model image for the ML model. Other embodiments are disclosed.

公开(公告)号：US20230101955A1

公开(公告)日：2023-03-30

申请号：US17486798

申请日：2021-09-27

Applicant: AT&T Intellectual Property I, L.P.

Inventor： Cuong Vo ,  Jeremy Fix ,  Jeffrey Dix ,  Eric Zavesky ,  Abhay Dabholkar ,  Rudolph Mappus ,  James Pratt

IPC: G06N20/00 ,  G06K9/62

Abstract: A method performed by a processing system including at least one processor includes defining a proposal for a proposed machine learning model, identifying an existing machine learning model, where the existing machine learning model shares a similarity with the proposed machine learning model, evaluating a fitness of the existing machine learning model for reuse in building the proposed machine learning model, building a new machine learning model that is consistent with the proposal for the proposed machine learning model by reusing a portion of the existing machine learning model, and monitoring a performance of the new machine learning model in a deployment environment.

公开(公告)号：US20220391745A1

公开(公告)日：2022-12-08

申请号：US17336573

申请日：2021-06-02

Applicant: AT&T Intellectual Property I, L.P. ,  AT&T Mobility II LLC

Inventor： Chris Vo ,  Abhay Dabholkar ,  Jeffrey Dix ,  Waicheng Moo ,  Hunter Kempf

IPC: G06N20/00 ,  G06N5/04 ,  G06F11/34

Abstract: Aspects of the subject disclosure may include, for example, a non-transitory, machine-readable medium, comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations including selecting modeling logic for an artificial intelligence (AI) model that solves a use case of a plurality of use cases; executing the AI model using holdout data to obtain a sub-result; evaluating the sub-result based on an evaluation metric; and combining the sub-result with other sub-results of the plurality of use cases to determine whether an exit criteria has been met. Other embodiments are disclosed.

公开(公告)号：US20210334078A1

公开(公告)日：2021-10-28

申请号：US16858225

申请日：2020-04-24

Applicant: AT&T Intellectual Property I, L.P.

Inventor： Jeffrey B. Saxon ,  Jeffrey Dix

IPC: G06F8/35 ,  H04L29/08 ,  G06F8/61 ,  G06N20/00

Abstract: Aspects of the subject disclosure may include, for example, a device, including a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations including receiving user specified metadata for execution tasks associated with a machine learning (ML) model; receiving artifacts specifying program code for implementing the ML model; creating a file system structure for a container to hold the ML model; receiving environment variables for operation of the ML model; and building the container including a model image for the ML model. Other embodiments are disclosed.