BlogsDatadogAI-driven Alerting UX

AI-driven Alerting UX

AI-driven Alerting UX

1
posts
2019

Datadog is evolving its alerting UX by introducing AI-driven capabilities such as forecasting, anomaly detection, and outlier detection. These methods aim to move beyond static thresholds by adapting to changing conditions, predicting future metric states, and identifying deviations from normal behavior. Algorithmic feeds are also being explored as a way to surface anomalous and outlier behaviors without explicit user configuration, representing a significant shift from opt-in alerting.

2019

Rethinking UX for AI-driven alerting | Datadog

1/22/2019

This post details the evolution of Datadog's alerting UX, moving from traditional static thresholds to AI-driven approaches. It introduces forecasting, anomaly detection, and outlier detection as new methods for generating alerts. Forecasting predicts when a threshold will be reached, anomaly detection identifies deviations from normal behavior, and outlier detection flags dissimilar items within a group. The post also discusses the concept of algorithmic feeds, which can surface important events without explicit user configuration.