Zebrium announced the addition of plain language root cause summarization derived from logs and metrics.
Powered by the GPT-3 language model, this game-changing feature constructs simple-to-understand summaries that help developers and SREs determine incident root cause, regardless of experience level.
Zebrium's ML solution has already successfully found the root cause for over 2,000 incidents in applications deployed with Kubernetes. The new approach speeds up the process of solving application and infrastructure incidents from hours to minutes and has proven to be highly accurate at identifying relevant root cause indicators.
In addition, Zebrium has expanded its integrations with observability and incident management tools to include Atlassian Opsgenie and Jira, complementing existing integrations with Slack, PagerDuty and the Elastic Stack.
These features add important new capabilities for incident response teams at a time when cloud-native applications are evolving faster, becoming increasingly distributed and failing in new ways, making it harder to troubleshoot and resolve incidents. While most DevOps teams today have many tools that can automatically detect software problems, finding root cause is still a manual and slow process of hunting through dashboards and logs to piece together what happened. Instead, Zebrium utilizes unsupervised machine learning to analyze logs and metrics to determine the root cause of application failures. It can also proactively detect new (unknown) failure modes that other tools miss.
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