Using AI for more automation in failure analysis
summary
The talk explains using machine learning (both supervised and unsupervised methods) to automate and speed up error analysis and anomaly detection in IT monitoring and network traffic. It presents practical implementation challenges and benefits illustrated with the Würth-Phoenix NetEye monitoring solution, and notes more improvements expected soon.
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chapters (10)
- 0:00Introduction
- 4:16From alerts to analysis
- 10:33Anomaly detection in practice
- 15:55Live demo: Performance dashboard
- 18:02Predicting system behavior
- 20:27AI for UX monitoring
- 25:22ML for performance analysis
- 28:32Deep vs classic ML
- 31:04Summary and outlook
- 32:50Q&A
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