This webinar gives a behind-the-scenes look at how Anodot uses its own product to monitor its customers’ health and engagement. More than 60% of tickets handled by Anodot’s support team are generated from monitoring alerts that notify us about real-time changes in our customer’s data rate and data usage. This immediacy allows our customer support team to take a proactive approach, as we often find issues before customers do.

If you’re already using Kubernetes, you’ve clearly made a commitment to digital transformation. So it hardly makes sense to be manually setting alerts, a key process in your AIOps workflow. AI monitoring is a must for a fully autonomous workflow.

Use AI to monitor your customer experience in real-time. Find and fix issues before they impact your usage, conversions, retention and revenues.

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.