Anomaly Detection

CloudWatch Anomaly Detection uses machine learning to spot unusual metric patterns without you having to set static thresholds. It learns what’s “normal” from historical data and alerts you when values fall outside this range. This helps reduce false alarms and the need for constant manual adjustments. It’s easy to enable: just pick your metric, turn on anomaly detection, and adjust the bandwidth if needed. You can set up alarms to notify you when anomalies are detected.

Transcript

Welcome back. Let's talk about one of the coolest, but maybe least known CloudWatch features out there: anomaly detection. But before we jump into the details, let's revisit or challenges that we had in the traditional monitoring approach. Purchase the full course to access the complete transcript.

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13:13

Major Learnings

•Anomaly Detection uses historical data and machine learning to define normal metric ranges automatically.
•It avoids the problems of static thresholds and reduces false alarms or missed issues.
•You can enable it with one click in CloudWatch and fine-tune the sensitivity by adjusting the bandwidth.
•Alarms can be set to trigger when metrics fall outside the predicted normal range, making monitoring more reliable.

Next Steps

1.Identify your key metrics and enable anomaly detection for them in CloudWatch.
2.Experiment with different bandwidth settings to balance sensitivity and noise.
3.Set up alarms for anomaly-based metrics to get notified of unusual behavior.
4.Monitor results over time and adjust as needed for your environment.