Datadog` applies the principles of `AIOps` (Artificial Intelligence for Operations) to its monitoring solutions, enabling proactive detection of issues across entire technology stacks. This approach combines big data and machine learning to automate IT operations processes, such as event correlation, anomaly detection, and causality determination. The company's AIOps-powered products help users proactively detect anomalies early on, reducing time spent investigating and resolving issues, consolidating related alerts, building automated troubleshooting workflows, and more. Specifically, `Datadog` focuses on proactive anomaly detection, using machine learning algorithms to scale anomaly detection effectively. These algorithms, including `Basic`, `Agile`, and `Robust`, enable users to detect anomalies early, before they become full-blown incidents. The `Basic` algorithm is suitable for unpredictable metrics, while the `Agile` algorithm is better suited for metrics with seasonal patterns. The `Robust` algorithm is designed for metrics with stable, recurring seasonal patterns. Additionally, `Watchdog`, an AI-powered engine, uses these algorithms to automatically flag anomalies and outliers, forecast potential bottlenecks, conduct automated business impact analysis and root cause analysis (RCA), and detect faulty code deployments. Users can customize anomaly detection, outlier detection, and forecasting alerts with `Datadog` monitors, configuring custom alerts based on their chosen metrics. By leveraging these features, users can proactively address issues before they become incidents, prevent unplanned downtime, and protect their environment.