US 11,688,111 B2
Visualization of a model selection process in an automated model selection system
Dakuo Wang, Cambridge, MA (US); Bei Chen, Blanchardstown (IE); Ji Hui Yang, Beijing (CN); Abel Valente, Buenos Aires (AR); Arunima Chaudhary, Boston, MA (US); Chuang Gan, Cambridge, MA (US); John Dillon Eversman, Austin, TX (US); Voranouth Supadulya, Round Rock, TX (US); Daniel Karl I. Weidele, Cambridge, MA (US); Jun Wang, Xi'an (CN); Jing James Xu, Xi'an (CN); Dhavalkumar C. Patel, White Plains, NY (US); Long Vu, Chappaqua, NY (US); Syed Yousaf Shah, Yorktown Heights, NY (US); and Si Er Han, Xi'an (CN)
Assigned to INTERNATIONAL BUSINESS MACHINES CORPORATION, Armonk, NY (US)
Filed by International Business Machines Corporation, Armonk, NY (US)
Filed on Jul. 29, 2020, as Appl. No. 16/942,284.
Prior Publication US 2022/0036610 A1, Feb. 3, 2022
Int. Cl. G06T 11/20 (2006.01); G06F 3/0481 (2022.01); G06N 20/00 (2019.01); G06N 5/00 (2023.01)
CPC G06T 11/206 (2013.01) [G06F 3/0481 (2013.01); G06N 5/00 (2013.01); G06N 20/00 (2019.01)] 19 Claims
OG exemplary drawing
 
1. A system, comprising:
a memory that stores computer executable components; and
a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise:
an interaction backend handler component that obtains one or more assessment metrics of a first model pipeline candidate and second model pipeline candidate, wherein the one or more assessment metrics comprises a percentage of training data allocated to a pipeline candidate by an automated pipeline selection process;
a visualization render component that concurrently renders a progress visualization of the first model pipeline candidate and a second progress visualization of the second model pipeline candidate based on the one or more assessment metrics, and wherein the progress visualization comprises a rendering in dots inside of other dots to indicate evaluation or training is in progress, a first type of line to indicate selected model pipeline candidates and a second type of line to indicate discarded model pipeline candidates; and
wherein the one or more assessment metrics is a build time metric, and wherein the first model pipeline candidate comprises a first combination of a machine learning model, a transformer and an estimator and wherein the second model pipeline candidate comprises a second combination of a machine learning model, a transformer and an estimator.