Elastic machine learning anomaly scoring has changed in 6.5. Learn how the new scoring relates to the normalization of partitions and multi-bucket anomalies.
Learn the differences between these two types of analysis via a practical use case involving document access and potential information stealing.
Optimize your results of your Machine Learning jobs by taking control of which data gets analyzed. Customize the datafeed with filters to get focused results.
Leverage the power of complex elasticsearch aggregation queries for your ML jobs. Follow this example of using a derivative aggregation to see how it works.
A complete breakdown of how machine learning in X-Pack scores anomalies and ranks them automatically on a severity scale from zero to one hundred.
Connecting Machine Learning to X-Pack Alerting for notification of anomalies is now easy in version 5.5