There are three components to Ze that involve what you'd think of as ML. We use a few different "classical" techniques together; our focus is on keeping costs d

There are three components to Ze that involve what you'd think of as ML. We use a few different "classical" techniques together; our focus is on keeping costs down while providing useful results on day one on even a newly-deployed custom application. First is the structuring of logs: we have a four stage pipeline for structuring and each stage has greater importance depending on how many ground truth instances there are of a given event type in the dataset (these are unlabeled, of course). These stages include heuristics, reachability clustering, a naive Bayes classifier with global fitness function, and a modified LCS. When the data comes in it is laid down in tables with typed columns directly, without post-processing; later, table merges are considered asynchronously. This lets you start doing anomaly detection really well right away. Next is anomaly detection. There are lots of dimensions we consider for AD; at the end of the day most of them boil down to some reflection of either “badness" or "rareness". We'd rather catch too many anomalies than miss one. This AD is run on both logs and metrics. Finally, there's incident detection: here we look primarily at how much independence is there between different streams w.r.t. their anomalies, and when we see an unusually high correlation across channels, we raise an incident. Here, a naive Bayes model is used to set cutoffs, with streams of anomalies considered as point processes. You can provide feedback here for training, but it is optional.