In the concluding part of a series on building a statistical anomaly detector in Elasticsearch, the author, Zachary Tong, demonstrates how to automate the anomaly detection process using Elasticsearch's Watcher plugin. The initial steps involved creating pipeline aggregations to identify the top 90th percentile of "surprise" values across datasets and using Timelion to visualize these values. The final step integrates these components into the Watcher, enabling real-time alerting and notifications. The process is split into two watches; the first collects surprise data hourly, while the second constructs a dynamic threshold and checks for anomalies, raising alerts if necessary. This approach showcases the power and flexibility of Elasticsearch's tools, allowing for ongoing adjustments and enhancements to the monitoring system.