We use unassisted machine learning to detect critical software problems. We show you a breakdown of issues and guide you to resolution. Our ML-based anomaly detection alerts you of new critical issues as soon as they happen. And we make it dead-simple to be alerted on issues you know about.
While log management and monitoring tools are good at finding pre-defined symptoms, Zebrium auto-detects anomalies, known issues, exceptions and errors without you having to do anything.
Examples of issues our anomaly detection found:
We use machine learning to perfectly structure logs and metrics. Each log line is schematized by event type, with parameters captured into their own typed columns and optimized for query. This results in a foundational “dictionary” of unique event types that is used to accurately learn normal patterns and reliably detect “anomalies” - when events break pattern. We do not require software changes, scripting or pre-training and everything is self-managed across software versions.