	US 12,190,210 B2
	Configurable and scalable machine learning model lifecycle operator
Benjamin Herta, Poughquag, NY (US); Darrell Christopher Reimer, Tarrytown, NY (US); Evelyn Duesterwald, Millwood, NY (US); Gaodan Fang, Elmsford, NY (US); Punleuk Oum, Tarrytown, NY (US); Debashish Saha, White Plains, NY (US); and Archit Verma, New York, NY (US)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by International Business Machines Corporation, Armonk, NY (US)
Filed on Dec. 17, 2021, as Appl. No. 17/554,166.
Prior Publication US 2023/0196178 A1, Jun. 22, 2023
Int. Cl. G06F 9/50 (2006.01); G06F 9/48 (2006.01); G06N 20/00 (2019.01)

CPC G06N 20/00 (2019.01) [G06F 9/485 (2013.01); G06F 9/4887 (2013.01); G06F 9/5038 (2013.01)]

17 Claims

1. A method of using a computing device to manage a lifecycle of machine learning models, the method comprising:

receiving, by a computing device, a plurality of pre-defined machine learning lifecycle tasks that represent stages in the lifecycle of the machine learning models, wherein inputs for pre-defined machine learning lifecycle tasks are provided by outputs of preceding pre-defined machine learning lifecycle tasks from the plurality of pre-defined machine learning lifecycle tasks;

managing, by the computing device, execution of a management-layer software layer for the plurality of pre-defined machine learning lifecycle tasks;

generating and updating, by the computing device, a machine learning pipeline using the management-layer software layer;

adding, by the computing device, new lifecycle tasks to the plurality of pre-defined machine learning lifecycle tasks;

monitoring, by the computing device, external dependencies; and

launching, by the computing device, pipelines containing a subset of the machine learning lifecycle tasks, wherein the launched pipelines update machine learning model instances, and wherein the updates generate an end-to-end lifecycle of the machine learning model instances associated with a corresponding desired state.