Anomaly detection of model performance in an MLOps platform

An apparatus, comprising:

• one or more network interfaces;
• a processor coupled to the network interfaces and configured to execute one or more processes;
• and
• a memory configured to store a process executable by the processor, the process when executed configured to:
• track performance of a machine learning model over time, wherein the machine learning model is used to monitor one or more computer networks based on data collected from the one or more computer networks;
• track performance metrics associated with training of the machine learning model, wherein the performance metrics are indicative of accuracy of the machine learning model as observed during training and testing prior to deployment;
• track changes in the data collected from the one or more networks;
• determine that a degradation of the performance of the machine learning model is anomalous by correlating the performance of the machine learning model over time with the performance metrics associated with training of the machine learning model and the tracked changes in the data collected from the one or more networks;
• and
• initiate a corrective measure for the degradation of the performance, in response to determining that the degradation of the performance is anomalous.

A tangible, non-transitory, computer-readable medium storing program instructions that cause a service to execute a process comprising:

• tracking, by the service, performance of a machine learning model over time, wherein the machine learning model is used to monitor one or more computer networks based on data collected from the one or more computer networks;
• tracking, by the service, performance metrics associated with training of the machine learning model, wherein the performance metrics are indicative of accuracy of the machine learning model as observed during training and testing prior to deployment;
• tracking, by the service, changes in the data collected from the one or more networks;
• determining, by the service, that a degradation of the performance of the machine learning model is anomalous by correlating the performance of the machine learning model over time with the performance metrics associated with training of the machine learning model and the tracked changes in the data collected from the one or more networks;
• and
• initiating, by the service, a corrective measure for the degradation of the performance, in response to determining that the degradation of the performance is anomalous.