Augmenting results with user annotations for Elastic machine learning

User annotations in Elasticsearch, introduced from version 6.6, provide a method for enhancing machine learning jobs with user-specific domain knowledge, helping to better interpret anomalies detected in data. These annotations can be applied to various datasets, such as weather sensor data, to highlight significant events or validate machine learning outputs against historical data. The annotations can be managed through the Single Metric Viewer, which allows users to create, edit, or delete annotations and share them with others via permalinks. Additionally, annotations are stored in a dedicated Elasticsearch index, making them accessible for automated processes, including programmatically creating annotations using Elasticsearch APIs. The integration with Watcher allows users to create curated alerts based on these annotations, sending notifications to platforms like Slack. This feature not only assists in anomaly detection but also improves alerting precision, enhancing the overall utility of machine learning applications in Elasticsearch.

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