Invention Grant
- Patent Title: Machine learning model representation and execution
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Application No.: US17835121Application Date: 2022-06-08
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Publication No.: US11620113B2Publication Date: 2023-04-04
- Inventor: Jeffrey B. Saxon , Jeffrey Dix
- Applicant: AT&T Intellectual Property I, L.P.
- Applicant Address: US GA Atlanta
- Assignee: AT&T Intellectual Property I, L.P.
- Current Assignee: AT&T Intellectual Property I, L.P.
- Current Assignee Address: US GA Atlanta
- Agency: Guntin & Gust, PLC
- Agent Robert Gingher
- Main IPC: G06F8/35
- 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.
Public/Granted literature
- US20220300259A1 MACHINE LEARNING MODEL REPRESENTATION AND EXECUTION Public/Granted day:2022-09-22
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