Adaptive training of machine learning models based on live performance metrics

An apparatus, comprising:

• one or more network interfaces;
• a processor coupled to the one or more network interfaces;
• and
• a memory configured to store a process that is executable by the processor, the process when executed configured to:
• deploy a first machine learning model to an inference location in a network, wherein the first machine learning model is used at the inference location to make inferences about the network;
• receive, from the inference location, an indication that the first machine learning model is exhibiting poor performance;
• identify a corrective measure for the poor performance that minimizes resource consumption by a model training pipeline of the apparatus;
• and
• deploy, based on the corrective measure, a second machine learning model to the inference location, wherein the second machine learning model is used in is lieu of the first machine learning model to make the inferences about the network.

A tangible, non-transitory, computer-readable medium storing program instructions that cause a device to execute a process comprising:

• deploying, by the device, a first machine learning model to an inference location in a network, wherein the first machine learning model is used at the inference location to make inferences about the network;
• receiving, at a device and from the inference location, an indication that the first machine learning model is exhibiting poor performance;
• identifying, by the device, a corrective measure for the poor performance that minimizes resource consumption by a model training pipeline of the device;
• and
• deploying, by the device and based on the corrective measure, a second machine learning model to the inference location, wherein the second machine learning model is used in lieu of the first machine learning model to make the inferences about the network.