Abstract: Technologies for monitoring input feature health for machine learning models and detecting anomalies are described. Embodiments include receiving, by a trained machine learning model and from an online system, a set of historical values and a current feature value of a time series feature, the time series feature represents one or more measured values of the online system. Embodiments include predicting an expected feature value and an expected range of values using the set of historical values. Embodiments include receiving the expected feature value and the expected range of values. Embodiments include receiving the current feature value. Embodiments include determining that an anomaly condition is present based on the current feature value, the expected feature value, and the expected range of values. Embodiments include generating an alert for the anomaly condition that includes the severity metric and the duration metric.