Abstract: Techniques facilitating anomaly detection and root cause analysis using distributed trace data. In one example, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components comprise: a preprocessing component; and a monitor component. The preprocessing component can generate a trace frame comprising a vectorized representation of textual trace data produced by microservices of a microservice application. The monitor component can identify a state of the microservice application using the trace frame.

Abstract: The present disclosure provides a method and system for adapting a machine learning model, such as an object detection model, to account for domain shift. The method includes receiving a labeled data elements and target image samples and performing a plurality of model adaptation epochs. Each adaptation epoch includes: predicting for each of the target image samples, using the machine learning model configured by a current set of configuration parameters, a corresponding target class label for the respective target data object included in the target image sample; generating a plurality of labeled mixed data elements that each include: (i) a mixed image sample including a source data object from one of the source image samples and a target data object from one of the target image samples, and (ii) the corresponding source class label for the source data object and the corresponding target class label for the target data object.