US 12,455,705 B2
Optimizing dataset transformations for use by machine learning models
Brian Gold, Los Altos, CA (US); Emily Watkins, Houston, TX (US); Ivan Jibaja, San Jose, CA (US); Igor Ostrovsky, Mountain View, CA (US); and Roy Kim, Los Altos, CA (US)
Assigned to PURE STORAGE, INC., Santa Clara, CA (US)
Filed by PURE STORAGE, INC., Santa Clara, CA (US)
Filed on Sep. 12, 2023, as Appl. No. 18/465,710.
Application 18/465,710 is a continuation of application No. 18/146,807, filed on Dec. 27, 2022, granted, now 11,768,636.
Application 18/146,807 is a continuation of application No. 16/888,402, filed on May 29, 2020, granted, now 11,556,280, issued on Jan. 17, 2023.
Application 16/888,402 is a continuation of application No. 16/040,996, filed on Jul. 20, 2018, granted, now 10,671,435, issued on Jun. 2, 2020.
Claims priority of provisional application 62/650,736, filed on Mar. 30, 2018.
Claims priority of provisional application 62/648,368, filed on Mar. 26, 2018.
Claims priority of provisional application 62/620,286, filed on Jan. 22, 2018.
Claims priority of provisional application 62/576,523, filed on Oct. 24, 2017.
Claims priority of provisional application 62/574,534, filed on Oct. 19, 2017.
Prior Publication US 2024/0028266 A1, Jan. 25, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 3/06 (2006.01); G06F 9/48 (2006.01); G06F 9/50 (2006.01); G06F 16/178 (2019.01); G06F 16/245 (2019.01); G06N 3/063 (2023.01); G06N 3/08 (2023.01); G06N 20/00 (2019.01); G06Q 30/0242 (2023.01); G06T 1/20 (2006.01); G06T 1/60 (2006.01); G06F 16/248 (2019.01); G06F 16/958 (2019.01)
CPC G06F 3/0679 (2013.01) [G06F 3/0604 (2013.01); G06F 3/0608 (2013.01); G06F 3/0646 (2013.01); G06F 3/0649 (2013.01); G06F 3/067 (2013.01); G06F 9/4881 (2013.01); G06F 9/5027 (2013.01); G06F 16/1794 (2019.01); G06F 16/245 (2019.01); G06N 3/063 (2013.01); G06N 3/08 (2013.01); G06N 20/00 (2019.01); G06Q 30/0243 (2013.01); G06T 1/20 (2013.01); G06T 1/60 (2013.01); G06F 16/248 (2019.01); G06F 16/972 (2019.01); G06T 2200/28 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
transforming, by a management plane associated with a storage system of one or more storage systems, a dataset using a transformation that is performed based on a format that is identified by the storage system as an expected format of input data for a machine learning model into a transformed dataset, wherein the transformed dataset in the identified expected format is more efficient for use by the machine learning model compared to another format in which the storage system stores the dataset;
scheduling, by the management plane associated with the storage system, one or more machine learning algorithms associated with the machine learning model at one or more servers capable of executing the machine learning model; and
transmitting, by the management plane associated with the storage system, the transformed dataset from the storage system to memory of the one or more servers capable of executing the machine learning model, wherein the transformed dataset includes data in the expected format.