Machine learning with distributed training

A method comprising:

• receiving, by a scheduler device of a remote network management platform, a machine learning (ML) training request from a particular computational instance, wherein the particular computational instance is one of a plurality of computational instances, on the remote network management platform, dedicated to respective managed networks, and wherein the remote network management platform also includes a plurality of ML trainer devices, configured to execute ML trainer processes;
• assigning, by the scheduler device, the ML training request to a particular ML trainer process of the 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 particular computational instance to the particular ML trainer device, wherein the identifier enables direct communication between the particular ML trainer device and the particular computational instance;
• wherein the assigning of the ML training request causes the particular ML trainer device of the plurality of ML trainer devices to:
• receive the training data from the particular computational instance, train the ML model using the particular ML trainer process in accordance with the received training data and the target variable, and provide the ML model as trained to the particular computational instance for local use on the computational instance.

An article of manufacture including a non-transitory computer-readable medium, having stored thereon program instructions that, upon execution by a scheduler device of a remote network management platform, cause the scheduler device to perform operations comprising:

• receiving a machine learning (ML) training request from a particular computational instance, wherein the particular computational instance is one of a plurality of computational instances, on the remote network management platform, dedicated to respective managed networks, and wherein the remote network management platform also includes a plurality of ML trainer devices, configured to execute ML trainer processes;
• assigning the ML training request to a particular ML trainer process of the 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 particular computational instance to the particular ML trainer device, wherein the identifier enables direct communication between the particular ML trainer device and the particular computational instance;
• wherein the assigning of the ML training request causes a particular ML trainer device of the plurality of ML trainer devices to:
• receive the training data from the particular computational instance, train the ML model using the particular ML trainer process in accordance with the received training data and the target variable, and provide the ML model as trained to the particular computational instance for local use on the computational instance.