Manufacturing optimization using a multi-tenant machine learning platform

1. A method comprising:

obtaining first physical sensor data, by a first printing machine associated with a first tenant of a multi-tenant machine learning platform;

comparing a print command sent to the first printing machine with a resultant material printed by the first printing machine;

determining, by a first machine learning spoke system associated with the first tenant, a first machine learning parameter based on at least the first physical sensor data, the first machine learning parameter comprising information pertaining to the comparison of the print command sent to the first printing machine with the resultant material printed by the first printing machine;

preventing exposure of the first physical sensor data of the first printing machine to any other tenant of the multi-tenant machine learning platform;

transmitting the first machine learning parameter from the first machine learning spoke system to a machine learning hub system of the multi-tenant machine learning platform; and

updating, by the machine learning hub system, a multi-tenant machine learning model based at least on the first machine learning parameter.

2. The method of claim 1 , further comprising:

obtaining second physical sensor data from a second manufacturing device associated with a second tenant of the multi-tenant machine learning platform;

determining, by a second machine learning spoke system associated with the second tenant, a second machine learning parameter based on at least the second physical sensor data;

preventing exposure of the second physical sensor data of the second manufacturing device to any other tenant of the multi-tenant machine learning platform;

transmitting the second machine learning parameter from the second machine learning spoke system to the machine learning hub system; and

updating, by the machine learning hub system, the multi-tenant machine learning model based at least on the second machine learning parameter.

3. The method of claim 2 , further comprising:

executing the multi-tenant machine learning model to determine a manufacturing optimization; and

adjusting the second manufacturing device, associated with the second tenant of the multi-tenant learning platform, based at least on the manufacturing optimization.

4. The method of claim 3 , further comprising:

transmitting the manufacturing optimization from the machine learning hub system to the second machine learning spoke system associated with the second tenant,

wherein adjusting the second manufacturing device is performed by the second machine learning spoke system responsive to receiving the manufacturing optimization from the machine learning hub system.

5. The method of claim 3 , wherein the second tenant is subscribed to the multi-tenant machine learning platform to obtain the manufacturing optimization without contributing any machine learning parameter to the machine learning hub system.

6. The method of claim 1 , wherein obtaining the first physical sensor data comprises obtaining data from a laser sensor of the first printing machine.

7. A system comprising:

a first one or more processors; and

a first one or more non-transitory computer-readable media storing instructions that, when executed by the first one or more processors, cause a first machine learning spoke system associated with a first tenant of a multi-tenant machine learning platform to perform operations comprising:

obtaining first physical sensor data from a first printing machine;

comparing a print command sent to the first printing machine with resultant material printed by the first printing machine;

determining a first machine learning parameter based on at least the first physical sensor data, the first machine learning parameter comprising information pertaining to the comparison of the print command sent to the first printing machine with the resultant material printed by the first printing machine;

preventing exposure of the first physical sensor data of the first printing machine to any other tenant of the multi-tenant machine learning platform; and

transmitting the first machine learning parameter to a machine learning hub system of the multi-tenant machine learning platform,

wherein the machine learning hub system is configured to update a multi-tenant machine learning model based at least on the first machine learning parameter.

8. The system of claim 7 , further comprising:

a second one or more processors; and

a second one or more non-transitory computer-readable media storing instructions that, when executed by a second one or more processors, cause a second machine learning spoke system associated with a second tenant of the multi-tenant machine learning platform to perform operations comprising:

obtaining second physical sensor data from a second manufacturing device;

determining a second machine learning parameter based on at least the second physical sensor data;

preventing exposure of the second physical sensor data of the second manufacturing device to any other tenant of the multi-tenant machine learning platform; and

transmitting the second machine learning parameter to the machine learning hub system of the multi-tenant machine learning platform,

wherein the machine learning hub system is further configured to update the multi-tenant machine learning model based at least on the second machine learning parameter.

9. The system of claim 7 , further comprising:

a second one or more processors; and

a second one or more non-transitory computer-readable media storing instructions that, when executed by a second one or more processors, cause a second machine learning spoke system associated with a second tenant of the multi-tenant machine learning platform to perform operations comprising:

receiving a manufacturing optimization, generated using the multi-tenant machine learning model, from the machine learning hub system; and

adjusting a second manufacturing device based at least on the manufacturing optimization.

10. The system of claim 9 , wherein the second tenant is subscribed to the multi-tenant machine learning platform to obtain the manufacturing optimization without contributing any machine learning parameter to the machine learning hub system.

11. The system of claim 7 , wherein the first one or more processors and the first one or more non-transitory computer-readable media are components of the first printing machine.

12. A system comprising:

one or more processors; and

one or more non-transitory computer-readable media storing instructions that, when executed by the one or more processors, cause a machine learning hub system of a multi-tenant machine learning platform to perform operations comprising:

receiving a first machine learning parameter from a first machine learning spoke system associated with a first tenant of the multi-tenant machine learning platform, the first machine learning parameter having been generated by the first machine learning spoke system based at least on first physical sensor data obtained from a first printing machine;

preventing exposure of the first physical sensor data of the first printing machine to any other tenant of the multi-tenant machine learning platform; and

updating a multi-tenant machine learning model based at least on the first machine learning parameter.

13. The system of claim 12 , the one or more non-transitory computer-readable media further storing instructions that, when executed by the one or more processors, cause the machine learning hub system to perform operations comprising:

receiving a second machine learning parameter from a second machine learning spoke system associated with a second tenant of the multi-tenant machine learning platform, the second machine learning parameter having been generated by the second machine learning spoke system based at least on second physical sensor data obtained from a second manufacturing device, while preventing the second physical sensor data to any other tenant of the multi-tenant machine learning platform; and

updating the multi-tenant machine learning model based at least on the second machine learning parameter.

14. The system of claim 13 , wherein updating the multi-tenant machine learning model based at least on the first machine learning parameter and updating the multi-tenant machine learning model based at least on the second machine learning parameter comprises:

computing a machine learning adjustment as a function of both the first machine learning parameter and the second machine learning parameter; and

updating the multi-tenant machine learning model based on the machine learning adjustment.

15. The system of claim 12 , the one or more non-transitory computer-readable media further storing instructions that, when executed by the one or more processors, cause the machine learning hub system to perform operations comprising:

executing the multi-tenant machine learning model to determine a manufacturing optimization; and

transmitting the manufacturing optimization to a second machine learning spoke system associated with a second tenant of the multi-tenant machine learning platform,

wherein the second machine learning spoke system uses the manufacturing optimization to adjust a second manufacturing device.

16. The system of claim 15 , wherein the second tenant is subscribed to the multi-tenant machine learning platform to obtain the manufacturing optimization without contributing any machine learning parameter to the machine learning hub system.