Summary
Anomaly detection jobs are certainly useful on their own, but when combined with near real-time alerting, users can really harness the power of automated analysis – while also being confident about getting only alerts that are meaningful.
After a practical study of how to effectively capture the results of anomaly detection jobs with real-time alerts, we went through a comprehensive example of using the new Kibana alerting framework to easily define some intuitive alerts and we tested them with a realistic alerting scenario. We then witnessed how an expert user can leverage the full power of Watcher for advanced alerting techniques if Kibana alerting cannot satisfy the complex alerting requirements.
In the next chapter, we'll see how anomaly detection jobs can assist not only with alerting on important key performance indicators but also how Elastic ML's automated analysis of a broad set of data within a specific application context is the means to achieving...
