A team manages an Azure Machine Learning workspace and deploys a model to an endpoint.

During high traffic, the question is whether the endpoint is keeping up with demand. Requests per minute (B) tells you the actual request load on the endpoint, letting you correlate traffic spikes with degradation. Connections active (C) reveals whether the endpoint ' s connection pool is saturating — too many concurrent connections without adequate scaling causes queuing and timeouts. Request latency (E) is the gold-standard measure of user-perceived performance; rising latency under load is the clearest signal of degradation. Feature count (A) is a model-design attribute, not a runtime performance metric. Dataset size (D) is a training-time concern unrelated to endpoint performance. Azure Machine Learning online endpoints expose these metrics through Azure Monitor, and Microsoft recommends configuring alert rules on latency and request rate thresholds for all production endpoints.

Microsoft Learn Reference Topic: Monitor Azure Machine Learning online endpoints – Azure Monitor metrics for managed endpoints