US 12,265,461 B2
Intelligently optimized machine learning models
Joseph Kozhaya, Morrisville, NC (US); Elizabeth Spingola, Alexandria, VA (US); Paul Samuel Schweigert, Bayville, NY (US); and Marcos Araque Fiallos, Elmwood Park, IL (US)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by International Business Machines Corporation, Armonk, NY (US)
Filed on Mar. 14, 2022, as Appl. No. 17/693,710.
Prior Publication US 2023/0289276 A1, Sep. 14, 2023
Int. Cl. G06F 11/34 (2006.01); G06N 20/20 (2019.01)
CPC G06F 11/3428 (2013.01) [G06N 20/20 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for intelligent optimization of machine learning models, the method comprising:
extracting, by a processor, metadata from a training pipeline for a first machine learning model, wherein the first machine learning model is configured to a first computing environment, and wherein the first machine learning model comprises a convolutional neural network;
mapping, by the processor, the extracted metadata to one or more constraints associated with a second machine learning model configured to a second computing environment, and wherein the second machine learning model comprises a convolutional neural network;
training, by the processor, the second machine learning model configured to the second computing environment based, at least in part, on the mapped one or more constraints, with a dataset used to train the first machine learning model, wherein training comprises preprocessing a dataset by reducing dimension of images in a dataset and adjusting a number of pixels per filter in the convolution; and
comparing, by the processor, one or more performance metrics of the first machine learning model to one or more performance metrics of the trained second machine learning model configured to the second computing environment.