In many cluster environments, nodes in the same cluster share the same characteristics and output similar KPIs. While it’s OK to treat the entire cluster as a unit for the purpose of monitoring and anomaly detection, there are times when it becomes crucial to identify outliers within the cluster, and Anodot’s outlier detection can help with this.

Use these best practices to stay on top of your Kubernetes ecosystem, minimize downtime, and ensure high availability and performance.

Amazon recently embedded “ML anomaly detection” into their Quicksight solution. How does it measure up against Anodot's dedicated anomaly detection platform. I conducted a test to see how the solutions compared.

From biased training sets to problems with input features to not understanding context (i.e., ‘dumb AI’), problems abound when it comes to correctly and accurately tracking and monitoring machine learning models.

Learn how AI and machine learning are enabling critical capabilities, such as real-time anomaly detection and forecasting, to meet rising challenges in the telecom market.

To keep you up-to-date with what’s going on in anomaly detection, we keep an ongoing list of glitches. Here’s what happened in June.

Learn how AI and machine learning are enabling critical capabilities, such as real-time anomaly detection and forecasting, to meet rising challenges in the telecom market.

Learn why revenue monitoring is different to other types of monitoring, such as IT and App Performance. Only AI-based real-time revenue monitoring can ensure watertight revenue protection for your business.

There's a growing disconnect between the capabilities businesses need from their analytics and what traditional tools actually provide. Compared with AI-based anomaly detection, dashboards, reports and static thresholds can prolong time to detection and time to resolution in several key ways.