1. A method of monitoring a first neural network, comprising: monitoring, via a second neural network, an activity of the first neural network, the activity comprising at least one of an activity rate, an activity pattern, a resource use, or a combination thereof; detecting, via the second neural network, an exception condition when the monitored activity is outside a range of baseline activity, the exception condition indicating a potential for failure of at least a component of the first neural network; and performing, via the second neural network, an exception event based at least in part on the detected exception condition.