The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Mar. 11, 2025

Filed:

Sep. 21, 2018
Applicant:

Cloudera, Inc., Palo Alto, CA (US);

Inventors:

Gregorio Convertino, Sunnyvale, CA (US);

Tianyi Li, Blacksburg, VA (US);

Haley Allen Most, San Francisco, CA (US);

Wenbo Wang, San Bruno, CA (US);

Yi-Hsun Tsai, San Francisco, CA (US);

Michael Tristan Zajonc, Oakland, CA (US);

Michael John Lee Williams, Los Angeles, CA (US);

Assignee:

CLOUDERA, INC., Palo Alto, CA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06N 7/01 (2023.01); G06F 11/34 (2006.01); G06F 16/904 (2019.01); G06N 20/00 (2019.01);
U.S. Cl.
CPC ...
G06N 7/01 (2023.01); G06F 11/3447 (2013.01); G06F 16/904 (2019.01); G06N 20/00 (2019.01);
Abstract

Techniques are disclosed for facilitating the tuning of hyperparameter values during the development of machine learning (ML) models using visual analytics in a data science platform. In an example embodiment, a computer-implemented data science platform is configured to generate, and display to a user, interactive visualizations that dynamically change in response to user interaction. Using the introduced technique, a user can, for example, 1) tune hyperparameters through an iterative process using visual analytics to gain and use insights into how certain hyperparameters affect model performance and convergence, 2) leverage automation and recommendations along this process to optimize the tuning given available resources, 3) collaborate with peers, and 4) view costs associated with executing experiments during the tuning process.


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