Tiered anomaly detection

A system comprising:

• one or more processors;
• and
• a memory coupled to the one or more processors, wherein the memory is configured to provide the one or more processors with instructions which when executed cause the one or more processors to:
• for each corresponding configuration item type of a plurality of different configuration item types, train a corresponding multi-variate machine learning model of a plurality of multi-variate machine learning models to perform anomaly detection for a corresponding configuration item type of the plurality of different configuration item types;
• in response to detecting, via a univariate machine learning model, an anomaly associated with a specific configuration item type of the plurality of different configuration item types, initiate for the specific configuration item type an execution of a particular multi-variate machine learning model of the plurality of multi-variate machine learning models;
• and
• evaluate an output of the execution of the particular multi-variate machine learning model to determine an anomaly detection result.

A computer program product, the computer program product being embodied in a non-transitory computer readable storage medium and comprising computer instructions for:

• for each corresponding configuration item type of a plurality of different configuration item types, training a corresponding multi-variate machine learning model of a plurality of multi-variate machine learning models to perform anomaly detection for a corresponding configuration item type of the plurality of different configuration item types;
• in response to detecting, via a univariate machine learning model, an anomaly associated with a specific configuration item type of the plurality of different configuration item types, initiating for the specific configuration item type an execution of a particular multi-variate machine learning model of the plurality of multi-variate machine learning models;
• and
• evaluating an output of the execution of the particular multi-variate machine learning model to determine an anomaly detection result.