Machine learning with distributed training

Patent metadata

Field	Value
Publication number	US-11620571-B2
Application number	US-201916506827-A
Country	US
Kind code	B2
Filing date	Jul 9, 2019
Priority date	May 5, 2017
Publication date	Apr 4, 2023
Grant date	Apr 4, 2023

Abstract

A network system may include a plurality of trainer devices and a computing system disposed within a remote network management platform. The computing system may be configured to: receive, from a client device of a managed network, information indicating (i) training data that is to be used as basis for generating a machine learning (ML) model and (ii) a target variable to be predicted using the ML model; transmit an ML training request for reception by one of the plurality of trainer devices; provide the training data to a particular trainer device executing a particular ML trainer process that is serving the ML training request; receive, from the particular trainer device, the ML model that is generated based on the provided training data and according to the particular ML trainer process; predict the target variable using the ML model; and transmit, to the client device, information indicating the target variable.

First claim

What is claimed is:

1. A system, comprising:
a plurality of machine learning (ML) trainer devices; and
a scheduler device configured to:
receive an ML training request from a computational instance;
assign the ML training request to a particular ML trainer device of the plurality of ML trainer devices, wherein the ML training request identifies: training data to be used as basis for generating an ML model, and a target variable to be predicted using the ML model; and
provide an identifier of the computational instance to the particular ML trainer device, wherein the identifier enables direct communication between the particular ML trainer device and the computational instance;
wherein assigning the ML training request comprises causing the particular ML trainer device of the plurality of ML trainer devices to: receive the training data from the computational instance, train the ML model using the received training data to predict the target variable, and provide the trained ML model to the computational instance for local use on the computational instance.

2. The system of claim 1 , wherein the ML training request is based on information received by the computational instance from a client device of a managed network, and wherein the information received from the client device identifies the training data and the target variable.

3. The system of claim 2 , wherein the information received from the client device specifies a training time, wherein assigning the ML training request to the particular ML trainer device comprises assigning the particular ML trainer device to serve the ML training request at the specified training time.

4. The system of claim 1 , wherein the scheduler device is configured to:
make a determination that a location of the particular ML trainer device is within a threshold distance of a location of the computational instance, and wherein the scheduler device assigns the ML training request to the particular ML trainer device based at least on the determination.

5. The system of claim 1 , wherein the scheduler device is configured to:
make a determination that the particular ML trainer device is available to serve the ML training request, and wherein the scheduler device assigns the ML training request to the particular ML trainer device based at least on the determination.

6. The system of claim 1 , wherein the scheduler device is configured to:
determine a performance metric indicating performance of the particular ML trainer device, and
wherein the scheduler device assigns the ML training request to the particular ML trainer device based at least on the determined performance metric meeting a criteria.

7. The system of claim 1 , wherein the particular ML trainer device is configured to:
transmit to the computational instance, while training the ML model, a message indicating a status of the ML training request.

8. The system of claim 7 , wherein the computational instance is configured to:
transmit to a client device of a respectively managed network, while the particular ML trainer device is training the ML model, the message indicating the status of the ML training request.

9. The system of claim 1 , wherein the computational instance is configured to:
predict the target variable using the ML model; and
transmit, to a client device of a managed network, information indicating a predicted value of the target variable.

10. The system of claim 9 , wherein the client device operates a web browser, and wherein transmitting, to the client device, the information indicating the predicted value of the target variable comprises causing the web browser to display the information indicating the predicted value of the target variable.

11. The system of claim 1 , wherein the particular ML trainer device comprises a temporary data storage device, wherein the particular ML trainer device is configured to:
store the training data at the temporary data storage device while training the ML model; and
delete the training data from the temporary data storage device after completing the training of the ML model.

12. The system of claim 1 , wherein the particular ML trainer device is configured to:
receive a randomly generated bitstring associated with the ML training request, wherein the randomly generated bitstring is originally transmitted by the computational instance for reception by a second ML trainer device of the plurality of ML trainer devices;
transmit the randomly generated bitstring to the computational instance along with a request for the training data; and
receive the requested training data from the computational instance upon the computational instance verifying that the randomly generated bitstring transmitted by the particular ML trainer device is identical to the randomly generated bitstring originally transmitted by the computational instance.

13. The system of claim 1 , wherein the scheduler device is configured to:
receive a second ML training request from a second computational instance; and
assign the second ML training request to a second ML trainer device of the plurality of ML trainer devices, wherein the second ML training request identifies: second training data to be used as basis for generating a second ML model, and a second target variable to be predicted using the second ML model,
wherein assigning of the second ML training request comprises causing the second ML trainer device of the plurality of ML trainer devices to: receive the second training data from the second computational instance, train the second ML model using the received second training data to predict the second target variable, and provide the second ML model as trained to the second computational instance.

14. The system of claim 1 , wherein the scheduler device is configured to:
receive a second ML training request from a second computational instance; and
assign the second ML training request to the particular ML trainer device of the plurality of ML trainer devices, wherein the second ML training request identifies: second training data to be used as basis for generating a second ML model, and a second target variable to be predicted using the second ML model,
wherein assigning of the second ML training request comprises causing the particular ML trainer device of the plurality of ML trainer devices to: receive the second training data from the second computational instance, train the second ML model using the received second training data to predict the second target variable, and provide the second ML model as trained to the second computational instance.

15. A method comprising:
receiving, by a scheduler device of a remote network management platform, a machine learning (ML) training request from a computational instance, and wherein the remote network management platform comprises a plurality of ML trainer devices, configured to execute a plurality of ML trainer processes;
assigning, by the scheduler device, the ML training request to a particular ML trainer process of the plurality of ML trainer processes and a particular ML trainer device of the plurality of ML trainer devices, wherein the ML training request identifies: training data to be used as basis for generating an ML model, and a target variable to be predicted using the ML model; and
providing an identifier of the computational instance to the particular ML trainer device, wherein the identifier enables direct communication between the particular ML trainer device and the computational instance;
wherein assigning of the ML training request comprises causing the particular ML trainer device of the plurali

Assignees

• Servicenow Inc

Inventors

• Bendre Nikhil
• Ros Fernando
• Govindarajan Kannan
• Jayaraman Baskar
• Thakur Aniruddha
• Palapudi Sriram
• Karakusoglu Firat

Classifications

• G06F9/46

  Multiprogramming arrangements · CPC title

• H04L67/00

  Network arrangements or protocols for supporting network services or applications (user-to-user messaging H04L51/00; network arrangements, protocols or services for supporting real-time applications in data packet communications networks H04L65/00) · CPC title

• G06N20/00 Primary

  Machine learning · CPC title

• H04L41/12

  Discovery or management of network topologies · CPC title

• G06F40/20

  Natural language analysis (semantic analysis of natural language G06F40/30) · CPC title