Kibana version: 8.6.1 (ECK)
Elasticsearch version: 8.6.1 (ECK)
APM Server version: Fleet 8.6.1 (ECK)
APM Agent language and version:
Browser version: Chrome 110.0.5481.104
Original install method (e.g. download page, yum, deb, from source, etc.) and version: ECK
Fresh install or upgraded from other version?: Fresh
Looking for some general guidance regarding machine learning jobs. I have an ECK cluster with APM accepting OTEL data from a collector.
My goal is, using machine learning, to detect anomalies/outliers (I'm not sure which is the correct route here) in a specific field within an APM index. For example, I'd like to be able to check via API whether the build of a product took longer than it typically does. So a build runs, and upon completion it checks elastic to determine whether that particular build ID was an anomaly.
This all sounds simple (and probably is), but I'm not sure what path I should be taking to do this. The data is getting to Elastic, but I'm not sure how to analyze it. Should I use an anomaly detection job? Should I use a data frame analysis job? Should I not be using either? I have been able to successfully set up ongoing anomaly detection, but from what I can tell, the anomaly detection works off a mean,median, etc, of a specified bucket. So I'm not sure how to go about determining if just one specific data point is an anomaly.
Hoping this all makes sense. I'm new to all this so let me know if this doesn't make sense and I'll try and clarify.
Thanks in advance.