Abstract: A system subscribes to one or more inference topics to which inferences are published on behalf of trainable software models executing in a vehicle computing environment. The system receives inferences from the topics as the inferences are published to the topics and associates the inferences with one or more trainable software models to be monitored. Also, the system identifies instances of unexpected output based on comparison of received inferences, associated with a given model to be monitored, to expected inference values identified in a configuration file, stored in a vehicle memory and associated with the given model, and, responsive to identifying the unexpected output, devises a modification strategy for the model based on characteristics of the unexpected output.

Abstract: A system receives identification of a machine learning model, stored by a vehicle, that is ready for training. The system determines, based on a configuration file associated with the model, whether batch-based or live training is to be used to train the model and collects data for training, defined by a configuration file associated with the model. The system calls a learning as a service vehicle process to load a kernel and configures the kernel based on configuration data defined in the configuration file. The system receives notification from the learning as a service process that the training is complete. Additionally, the system validates a model, responsive to the notification including indication that training was successful, using a vehicle model validation process to test the model with live data before deployment by background execution of the model and saves a copy of the model for deployment responsive to successful validation.