eBay Inc.
MACHINE LEARNING-BASED INFRASTRUCTURE ANOMALY AND INCIDENT DETECTION USING MULTI-DIMENSIONAL MACHINE METRICS

Abstract:

A machine learning ("ML") pipeline that includes unsupervised learning, supervised learning, and Bayesian learning is utilized to train a ML classifier that can classify machine metrics as being indicative of an anomaly. A boosting process can be utilized during the unsupervised learning portion of the ML pipeline that scores clusters of training data for completeness, and further splits clusters of training data based upon the completeness scores in order to optimize the clustering of the training data. Supervised learning is then performed on the cluster-labeled training data. Bayesian learning can also be utilized to assign incident probability inferences to the clusters of training data. Once the ML classifier has been trained, the ML classifier can be utilized in a production environment to classify multi-dimensional machine metrics generated by computing devices in the production environment as being indicative of an anomaly.