US 12,353,420 B2
Techniques for providing synchronous and asynchronous data processing
Bryan James Phillippe, Fall City, WA (US); Ashok Nagarajan, Hillsboro, OR (US); Jeonghyeon Hwang, Elmhurst, NY (US); and John James Backof, II, Tiburon, CA (US)
Assigned to Oracle International Corporation, Redwood Shores, CA (US)
Filed by Oracle International Corporation, Redwood Shores, CA (US)
Filed on Jul. 28, 2021, as Appl. No. 17/387,795.
Prior Publication US 2023/0034196 A1, Feb. 2, 2023
Int. Cl. G06F 16/24 (2019.01); G06F 9/455 (2018.01); G06F 9/54 (2006.01); G06F 16/2455 (2019.01); G06N 5/04 (2023.01); G06N 20/00 (2019.01)
CPC G06F 16/24568 (2019.01) [G06F 9/45558 (2013.01); G06F 9/544 (2013.01); G06F 9/546 (2013.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01); G06F 2009/45595 (2013.01); G06F 2209/547 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method, comprising:
obtaining, by a machine-learning model service within a cloud-computing environment, input data corresponding to a request for output, the machine-learning model service executing a stream manager application and a machine-learning model via a common cloud-computing container, the machine-learning model service being configured to selectively process provided input data using a synchronous process or an asynchronous process;
providing, by the stream manager application via a local communication channel as part of the asynchronous process, the input data as input to the machine-learning model, wherein the local communication channel bypasses a local network interface hardware of a computing device on which the machine-learning model service executes;
receiving, by the stream manager application via the local communication channel, prediction results from the machine-learning model; and
providing, as part of the asynchronous process, the prediction results as output data in response to the request.