US 12,455,743 B2
Configurable pipelines for training and deploying machine learning processes in distributed computing environments
Alexander Clarence, Toronto (CA); Ding Tao Liu, Toronto (CA); Raunaq Suri, Mississauga (CA); Guangwei Yu, Toronto (CA); Maksims Volkovs, Toronto (CA); Satya Krishna Gorti, Toronto (CA); and Baiju Devani, Toronto (CA)
Assigned to The Toronto-Dominion Bank, Toronto (CA)
Filed by The Toronto-Dominion Bank, Toronto (CA)
Filed on Sep. 27, 2023, as Appl. No. 18/373,918.
Claims priority of provisional application 63/466,925, filed on May 16, 2023.
Prior Publication US 2024/0385838 A1, Nov. 21, 2024
Int. Cl. G06F 9/30 (2018.01); G06F 9/38 (2018.01)
CPC G06F 9/3005 (2013.01) [G06F 9/3836 (2013.01)] 20 Claims
OG exemplary drawing
 
1. An apparatus, comprising:
a memory storing instructions;
a communications interface; and
at least one processor coupled to the memory and the communications interface, the at least one processor being configured to execute the instructions to:
obtain, from the memory, elements of configuration data associated with a plurality of application engines and pipelining data characterizing a sequential execution of at least a subset of the application engines, at least one of the elements of configuration data being generated by a computing system;
based on the pipelining data, execute sequentially each of the subset of the application engines in accordance with corresponding ones of the elements of configuration data, the executed subset of the application engines causing the at least one processor to perform operations that at least one of (i) train a machine-learning or artificial-intelligence process or (ii) apply the trained machine-learning or artificial-intelligence process to an input dataset;
perform operations that obtain artifact data generated by the executed subset of the application engines and that store the artifact data within a portion of the memory; and
transmit at least a portion of the artifact data to the computing system via the communications interface.