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UID:pretalx-devopsdays-austin-2026-ZPRHEA@talks.devopsdays.org
DTSTART;TZID=CST:20260506T131000
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DESCRIPTION:Kubernetes clusters generate huge amounts of metrics\, logs\, a
 nd traces even when nothing is actually wrong. I wanted to see whether ano
 maly detection could help separate real problems from background noise. To
  test this\, I built a small Kubernetes cluster and intentionally broke it
  in different ways. The results were mixed: some failures were detected ea
 rly\, while normal behavior often triggered false alarms. This lightning t
 alk shares what anomaly detection is good at\, where it struggles\, and wh
 at DevOps teams should realistically expect when using it in Kubernetes en
 vironments.
DTSTAMP:20260410T013400Z
LOCATION:Ballroom
SUMMARY:When Kubernetes Gets Noisy: What Anomaly Detection Gets Right - Spo
 orthi Palakshaiah
URL:https://talks.devopsdays.org/devopsdays-austin-2026/talk/ZPRHEA/
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