US 12,333,442 B2
Intelligent updating and data processing for deployed machine learning models
Uttam B. Ramamurthy, Bangalor (IN); David Dellsperger, Kansas City, MO (US); Christopher S. Finn, Liberty, MO (US); Brandon Davis, Kansas City, MO (US); James Gritter, Parkville, MO (US); Stephen Patel, Kansas City, MO (US); Maulik Gandhi, De Pere, WA (US); and Adam Sabaliauskas, Lee's Summit, MO (US)
Assigned to Cerner Innovation, Inc., Kansas City, MO (US)
Filed by CERNER INNOVATION, INC., Kansas City, KS (US)
Filed on Jun. 22, 2021, as Appl. No. 17/354,693.
Prior Publication US 2022/0405619 A1, Dec. 22, 2022
Int. Cl. G06N 3/09 (2023.01); G06F 16/2455 (2019.01); G06N 3/096 (2023.01); G06N 5/04 (2023.01); G06N 20/00 (2019.01)
CPC G06N 3/096 (2023.01) [G06F 16/24568 (2019.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01)] 23 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
deriving a trained machine learning model using a set of training data;
deploying a first copy of the trained machine learning model to a first production environment;
subsequent to the deploying the first copy of the trained machine learning model:
based on a first mapping between (a) the first copy of the trained machine learning model and (b) a plurality of data source entities, maintaining a first computer object storing a first set of stream data corresponding to the first copy of the trained machine learning model;
at least partially in response to detecting a first triggering event corresponding to the first copy of the trained machine learning model:
accessing, at the first computer object, the first set of stream data; and
feeding the first set of stream data to the first copy of the trained machine learning model; and
generating a first estimate by the first copy of the trained machine learning model using the first set of stream data as input;
storing the first estimate to a second computer object storing a first plurality of estimates generated by the first copy of the trained machine learning model;
scanning at least one of: the first computer object or the second computer object;
in response to the scanning, detecting an anomaly with at least one of: the first set of stream data or the first estimate; and
based at least in part on the detecting of the anomaly and the first mapping, updating the first copy of the trained machine learning model for first production environment by feeding at least a second set of stream data, associated with the plurality of data sources, to the first copy of the trained machine learning model.