Large B2C systems generate a deluge of telemetry facts, ranging from CPU and memory records to logs, bug codes, and nuanced platform-specific alerts throughout respective areas.
This consultation provides a hands-on approach to anomaly detection using Python to improve MTTR and decrease alert fatigue by way of delivering precise, honest alerts. Drawing on actual-world experience in growing a wise incident management bot, we explore the way to process diverse, multivariate statistics streams from pods, services, and platforms like iOS, Android, and the web. The model correlates metrics throughout infrastructure layers and platforms to surface a significant alerts, complete with context, diagnostics, and counselled remediation steps proactively.
Topics covered encompass:
Correlating contextual alerts to lessen noise and alert fatigue
Boosting confidence in alerts through meaningful insights
Automating diagnostics to force faster RCA
In case you're running big scale, this session will share sensible, adaptable strategies to embed deep gaining of knowledge into your observability stack and transition from reactive monitoring to proactive decision-making.