IP Library › Granted Patent US 12,373,428
Granted Patent B2
US 12,373,428 · App. 17/660,735 · Granted Jul 29, 2025

Machine learning models in an artificial intelligence infrastructure

Inventors: Brian Gold (Los Altos, CA); Emily Watkins (Houston, TX); Ivan Jibaja (San Jose, CA); Igor Ostrovsky (Mountain View, CA); Roy Kim (Los Altos, CA)
Assignee: PURE STORAGE, INC.
G06F16/24534G06F3/06G06F3/061G06F3/0629G06F3/0647G06F16/2255G06F18/213G06N3/08G06N20/00G06T1/20G06T1/60G06T2200/28
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Quick Facts
Patent No.
US 12,373,428
App. No.
17/660,735
Filed
Apr 26, 2022
Granted
Jul 29, 2025
Kind
B2
Art Unit
2144
USPC
707/687
Abstract

Improving machine learning models in an artificial intelligence infrastructure includes: storing, within one or more storage systems of an artificial intelligence infrastructure, information describing a dataset and one or more transformations applied to the dataset resulting in a transformed dataset; and storing, within the one or more storage systems, information describing only portions of previous versions of a machine learning model that differ from a current version of the machine learning model, wherein the previous versions used the transformed dataset as input during one or more prior executions by the artificial intelligence infrastructure.

Claims (68)

1. A method comprising:

storing, within one or more storage systems of an artificial intelligence infrastructure, information describing a dataset and one or more transformations applied to the dataset resulting in a transformed dataset;

identifying, by the artificial intelligence infrastructure, previous versions of a machine learning model that used the transformed dataset as input during one or more prior executions by the artificial intelligence infrastructure;

storing, within the one or more storage systems, information describing only differences between the previous versions of the machine learning model and a current version of the machine learning model;

retaining one or more first portions of the transformed dataset associated with the current version of the machine learning model at a first storage tier; and

moving one or more second portions of the transformed dataset associated with the previous versions of the machine learning model to a second storage tier.

2. The method of claim 1 wherein the storing, within the one or more storage systems of the artificial intelligence infrastructure, the information describing the dataset and the one or more transformations applied to the dataset resulting in the transformed dataset further comprises:

generating, by the artificial intelligence infrastructure applying a predetermined hash function to the dataset, the one or more transformations applied to the dataset, and the transformed dataset, a hash value; and

storing, within the one or more storage systems, the hash value.

3. The method of claim 1 wherein the storing, within the one or more storage systems, information describing only differences between previous versions of the machine learning model and a current version of the machine learning model further comprises:

generating, by the artificial intelligence infrastructure applying a predetermined hash function to the previous versions of the machine learning model and the transformed dataset, a hash value; and

storing, within the one or more storage systems, the hash value.

4. The method of claim 1 further comprising:

identifying differences between the current version of the machine learning model and the previous versions of the machine learning model.

5. The method of claim 1 further comprising:

determining, by the artificial intelligence infrastructure, whether data related to one or more of the previous versions of the machine learning model should be tiered off of the one or more storage systems; and

responsive to determining that the data related to the one or more of the previous versions of the machine learning model should be tiered off of the one or more storage systems:

storing the data related to the one or more of the previous versions of the machine learning model in lower-tier storage; and

removing, from the one or more storage systems, the data related to the one or more of the previous versions of the machine learning model.

6. The method of claim 1 further comprising identifying, from amongst the previous versions and the current version of the machine learning model, a preferred version of the machine learning model.

7. The method of claim 1 further comprising tracking an improvement of a particular version of the machine learning model over time.

8. An artificial intelligence infrastructure comprising:

one or more storage systems;

one or more graphical processing unit (‘GPU’) servers; and

a processing device, operatively coupled to the one or more storage systems and one or more GPU servers, the processing device configured to:

store, within the one or more storage systems, information describing a dataset and one or more transformations applied to the dataset resulting in a transformed dataset;

obtain, by the artificial intelligence infrastructure, identifiers for previous versions of a machine learning model that used the transformed dataset as input during one or more prior executions by the artificial intelligence infrastructure;

store, within the one or more storage systems, information describing only differences between the previous versions of the machine learning model and a current version of the machine learning model;

retain one or more first portions of the transformed dataset associated with the current version of the machine learning model at a first storage tier; and

move one or more second portions of the transformed dataset associated with the previous versions of the machine learning model to a second storage tier.

9. The artificial intelligence infrastructure of claim 8 wherein to store, within the one or more storage systems, the information describing the dataset and the one or more transformations applied to the dataset resulting in the transformed dataset the processing device is further configured to:

generate, by the artificial intelligence infrastructure applying a predetermined hash function to the one or more transformations applied to the dataset and the transformed dataset, a hash value; and

store, within the one or more storage systems, the hash value.

10. The artificial intelligence infrastructure of claim 8 wherein to store, within the one or more storage systems, information describing only differences between previous versions of the machine learning model and a current version of the machine learning model the processing device is further configured to:

generate, by the artificial intelligence infrastructure applying a predetermined hash function to the previous versions of the machine learning model, a hash value; and

store, within the one or more storage systems, the hash value.

11. The artificial intelligence infrastructure of claim 8 wherein the processing device is further configured to:

identity, by a unified management plane, differences between the current version of the machine learning model and the previous versions of the machine learning model.

12. The artificial intelligence infrastructure of claim 8 wherein the processing device is further configured to:

determining, by the artificial intelligence infrastructure, whether data related to one or more of the previous versions of the machine learning model should be tiered off of the one or more storage systems; and

responsive to determining that the data related to the one or more of the previous versions of the machine learning model should be tiered off of the one or more storage systems:

storing the data related to the one or more of the previous versions of the machine learning model in lower-tier storage; and

removing, from the one or more storage systems, the data related to the one or more of the previous versions of the machine learning model.

13. The artificial intelligence infrastructure of claim 8 wherein the processing device is further configured to:

identify, from amongst the previous versions and the current version of the machine learning model, a preferred version of the machine learning model.

14. The artificial intelligence infrastructure of claim 8 wherein wherein the processing device is further configured to tracking an improvement of a particular version of the machine learning model over time.

15. An apparatus comprising:

a memory; and

a processing device, operatively coupled with the memory, the processing device configured to:

store, within one or more storage systems of an artificial intelligence infrastructure, information describing a dataset and one or more transformations applied to the dataset resulting in a transformed dataset;

obtain, by the artificial intelligence infrastructure, identifiers for previous versions of a machine learning model that used the transformed dataset as input during one or more prior executions by the artificial intelligence infrastructure;

store, within the one or more storage systems, information describing only differences between the previous versions of the machine learning model and a current version of the machine learning model;

retain one or more first portions of the transformed dataset associated with the current version of the machine learning model at a first storage tier; and

move one or more second portions of the transformed dataset associated with the previous versions of the machine learning model to a second storage tier.

16. The apparatus of claim 15 wherein the processing device is further configured to:

identify, by a unified management plane, differences between the current version of the machine learning model and the previous versions of the machine learning model.

17. The apparatus of claim 15 wherein the processing device is further configured to:

determining, by the artificial intelligence infrastructure, whether data related to one or more of the previous versions of the machine learning model should be tiered off of the one or more storage systems; and

responsive to determining that the data related to the one or more of the previous versions of the machine learning model should be tiered off of the one or more storage systems:

storing the data related to the one or more of the previous versions of the machine learning model in lower-tier storage; and

removing, from the one or more storage systems, the data related to the one or more of the previous versions of the machine learning model.

18. The apparatus of claim 15 wherein the processing device is further configured to:

identify, from amongst the previous versions and the current version of the machine learning model, a preferred version of the machine learning model.

19. The apparatus of claim 15 wherein the processing device is further configured to:

track an improvement of a particular version of the machine learning model over time.

20. The apparatus of claim 15 wherein the processing device is further configured to:

generate, by the artificial intelligence infrastructure applying a predetermined hash function to the dataset, and the one or more transformations applied to the dataset, a hash value; and

store, within the one or more storage systems, the hash value.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 25, 2022
From: GOLD, BRIAN; WATKINS, EMILY; JIBAJA, IVAN; OSTROVSKY, IGOR; KIM, ROY
To: PURE STORAGE, INC.
Reel/Frame 060017/0670 →
Continuity (9)
Continuation 16515698 · Jul 18, 2019
Continuation 16045814 · Jul 26, 2018
Provisional Application 62650736 · Mar 30, 2018
Provisional Application 62648368 · Mar 26, 2018
Provisional Application 62620286 · Jan 22, 2018
Provisional Application 62579057 · Oct 30, 2017
Provisional Application 62576523 · Oct 24, 2017
Provisional Application 62574534 · Oct 19, 2017
Related Publication 20220253443A1 · Aug 11, 2022
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