Abstract: The disclosure relates to systems and methods of detecting anomalies using a plurality of machine learning models. Each of the machine learning models may be trained to detect a respective behavior of historical data values for a given metric. Thus, a system may perform anomaly detection based on different behaviors of the same metric of data, reducing instances of false positive anomaly detection while also reducing instances of false negative reporting. The plurality of machine learning models may be trained to detect anomalies across multiple different types of metrics as well, providing robust multi-metric anomaly detection across a range of behaviors of historical data values. The system may implement a pluggable architecture for the plurality of machine learning models in which models may be added or removed from pluggable architecture. In this way, the system may detect anomalies using a configurable set of machine learning models.