Abstract: A service to provide anomaly detection receives a request to back-test the service. The request includes information for accessing a dataset of historical data. The service executes workflows to ingest the data, train a plurality of machine learning models to perform anomaly detection, and detect anomalies in the dataset. A representation of the detect anomalies is generated and presented to a user. The service receives an indication to activate the service to provide ongoing anomaly detection services.

Abstract: Systems and methods are described for using a recurring event based scheduler to continuously monitor data to detect anomalies within the data. In some aspects, an anomaly detection schedule may be determined for monitoring time series data to detect anomalies based on a data ingestion interval. A plurality of anomaly detection events may be sequentially generated and stored in an event queue at times specified by the anomaly detection schedule. The anomaly detection events may then be processed sequentially from the event queue to trigger execution of a plurality of anomaly detection workflow tasks at the times specified by the anomaly detection schedule. In some cases, execution of individual anomaly detection workflow tasks causes individual portions of time series data to be obtained from a customer data source and processed by an anomaly detection model to detect anomalies in the time series data.