# Clone - APAC

> Learn how to troubleshoot Kubernetes faster with practical root cause analysis techniques, incident response best practices, and real-world lessons learned.

Source: https://openobserve.ai/webinars-videos/launching-ai-projects-production-temp/
Duration: 45:00 minutes

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Join Kubernetes experts as they share lessons learned from real incidents, root cause analysis techniques, and ways to reduce time-to-answer.

## What you'll learn

- Why Kubernetes incidents take longer to resolve than expected
- How fragmented data across logs, metrics, traces, and cluster state slows time-to-answer
- What effective correlation looks like in real Kubernetes environments
- Common troubleshooting patterns and lessons learned from production incidents
- Best practices for reducing troubleshooting time and improving reliability
- Practical approaches to moving from alert to root cause faster

Kubernetes generates more signals than ever, yet finding answers often takes too long.

During incidents, logs, metrics, traces, and cluster state are spread across multiple tools and views. Teams spend valuable time switching context, manually correlating signals, and piecing together what happened before they can take action.

This session explores real-world troubleshooting lessons, root cause analysis techniques, and practical approaches for reducing time-to-answer in Kubernetes environments.

## Resources

- [Try OpenObserve](https://cloud.openobserve.ai/)
- [Unified Kubernetes Monitoring](https://openobserve.ai/kubernetes-monitoring/)