	US 12,154,015 B2
	Providing custom machine-learning models
Jonathan Daniel Keech, Kirkland, WA (US); Kesavan Shanmugam, Redmond, WA (US); Simon Calvert, Sammamish, WA (US); Mark A Wilson-Thomas, Mercer Island, WA (US); and Vivian Julia Lim, Redmond, WA (US)
Filed by Microsoft Technology Licensing, LLC, Redmond, WA (US)
Filed on Oct. 10, 2022, as Appl. No. 17/963,028.
Application 17/963,028 is a continuation of application No. 16/205,070, filed on Nov. 29, 2018, granted, now 11,475,370.
Prior Publication US 2023/0029481 A1, Feb. 2, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 8/33 (2018.01); G06N 20/20 (2019.01)

CPC G06N 20/20 (2019.01) [G06F 8/33 (2013.01)]

20 Claims

1. A method for providing custom machine learning models to a computer system, said method comprising:

accessing data for the computer system;

using the data to select a set of machine learning models that are determined to be applicable to the computer system, wherein the computer system is facilitating development of a codebase in a programming language such that machine learning models that are directed to codebase development are ones that are determined to be applicable to the computer system, and wherein determining which machine learning models are to be included in the set is performed by:

determining attributes associated with the data;

accessing a plurality of machine learning models;

using the attributes to filter the plurality of machine learning models, resulting in identification of machine learning models that are directed to codebase development, wherein said filtering involves identification of a particular machine learning model that detects variable misuses for codebases that are developed in said programming language;

using machine learning to build an additional model that is applicable to the computer system based on the determined attributes; and

including at least the built model and the particular machine learning model in the set of machine learning models; and

aggregating the set to generate an aggregated set of machine learning models that are customized to the computer system.