Operations | Monitoring | ITSM | DevOps | Cloud


Introducing Anomaly Advisor - Unsupervised Anomaly Detection in Netdata

Today we are excited to launch one of our flagship ML assisted troubleshooting features in Netdata – the Anomaly Advisor. The Anomaly Advisor builds on earlier work to introduce unsupervised anomaly detection capabilities into the Netdata Agent from v1.32.0 onwards.

Kubernetes throttling? It doesn't have to suck!

Kubernetes has a bad habit of throttling CPU resources—with the result that you can suffer severely degraded performance or find yourself paying a fortune for extra, unnecessary infrastructure. Watch this video to learn how K8s clusters protect themselves from what they see as heavy CPU usage, and how you can monitor and troubleshoot the problem. We demonstrate how you can:– Use Netdata to reduce API response times by a factor of 7– Expect to reduce infrastructure resource requirements by 60-75%

Kubernetes Throttling Doesn't Have To Suck. Let Us Help!

In the Kubernetes (K8s) community, there is a huge misconception about CPU allocation and utilization. Even highly experienced SREs find themselves struggling with the way Kubernetes allocates CPU resources, leading to misconfigured CPU allocations and extremely negative outcomes. For starters, this results in significant quality degradation on important service components, introduced by behind-the-scenes CPU limiting (or throttling).

Troubleshooting Alerts the Right Way: As a Team

At Netdata, we love two things more than anything else: Our goal is to make troubleshooting and monitoring as seamless as possible with the open-source Agent. This includes giving you pre-configured alerts so that you get notified immediately when a disruption occurs. The Netdata Agent comes with over 250 pre-configured and optimized alerts.

CNCF Live: Power up your machine learning - Automated anomaly detection

Our Analytics & ML lead Andrew Maguire recently had a chance to share our new Anomaly Advisor feature with the wider CNCF community. In his demonstration he did some light chaos engineering (using Gremlin and stress-ng) to generate some real anomalies on his infrastructure and watch how it all played out in the Anomaly Advisor in Netdata Cloud. There were also some great questions and discussion from the audience around ML in general and in the observability space itself.

Machine learning for infrastructure monitoring and troubleshooting, explained

Learn exactly what machine learning is and how it takes part in the observability, monitoring, and troubleshooting industry. We'll also cover the future of ML trends within the industry, and how Netdata is staying at the forefront of machine learning development.