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.

Abstract: Fault tolerance techniques for a plurality of nodes executing application thread groups include executing at least a portion of a first application thread group based on a delegation by a first node, wherein the first node delegates an execution of the first application thread group amongst the plurality of nodes and has a highest priority indicated by an ordered priority of the plurality of nodes. A failure of the first node can be identified based on the first node failing to respond to a message sent to it. A second node can then be identified as having a next highest priority indicated by the ordered priority such that the second node can delegate an execution of a second application thread group amongst the plurality of nodes.

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.

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.

Abstract: Fault tolerance techniques for a plurality of nodes executing application thread groups include executing at least a portion of a first application thread group based on a delegation by a first node, wherein the first node delegates an execution of the first application thread group amongst the plurality of nodes and has a highest priority indicated by an ordered priority of the plurality of nodes. A failure of the first node can be identified based on the first node failing to respond to a message sent to it. A second node can then be identified as having a next highest priority indicated by the ordered priority such that the second node can delegate an execution of a second application thread group amongst the plurality of nodes.

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.

Abstract: Fault tolerance techniques for a plurality of nodes executing application thread groups include executing at least a portion of a first application thread group based on a delegation by a first node, wherein the first node delegates an execution of the first application thread group amongst the plurality of nodes and has a highest priority indicated by an ordered priority of the plurality of nodes. A failure of the first node can be identified based on the first node failing to respond to a message sent to it. A second node can then be identified as having a next highest priority indicated by the ordered priority such that the second node can delegate an execution of a second application thread group amongst the plurality of nodes.

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.

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.

Abstract: Fault tolerance techniques for a plurality of nodes executing application thread groups include executing at least a portion of a first application thread group based on a delegation by a first node, wherein the first node delegates an execution of the first application thread group amongst the plurality of nodes and has a highest priority indicated by an ordered priority of the plurality of nodes. A failure of the first node can be identified based on the first node failing to respond to a message sent to it. A second node can then be identified as having a next highest priority indicated by the ordered priority such that the second node can delegate an execution of a second application thread group amongst the plurality of nodes.