Anomaly detection in Elastic's machine learning framework, particularly in version 8.6, enhances understanding of anomaly scoring by providing detailed insights into the scoring algorithm. The process involves the analysis of time series data, identifying trends, and distinguishing between anomalies and recurring patterns. Anomaly scores are influenced by single bucket impact, multi bucket impact, and anomaly characteristics impact, which consider factors like probability distributions and historical data patterns. Scores are normalized between 0 and 100, with adjustments made as new data arrives, often causing previous scores to be reduced when larger anomalies are detected. The detailed view in Kibana version 8.6 highlights these changes, providing clearer explanations for users. Additionally, the Elasticsearch Relevance Engine equips developers with tools for building AI-powered search applications, emphasizing the evolving capabilities of Elastic's platform for real-world applications.