Mixed cloud and premise based infrastructures, complex applications, agile DevOps environments and the need to identify advanced security threats result in an overwhelming volume of data. IT is a Big Data world and antiquated monitoring, troubleshooting and forensic approaches just won't scale.
Prelert's automated anomaly detection analytics are highly scalable; super advanced and leverage 100% unsupervised machine learning algorithms to cut through millions of data points in seconds.
You get the actionable insights you need to detect and resolve developing performance issues and security threats.
From the time you download until you get your first insights is a matter of minutes.
Prelert's machine learning algorithms process millions of datapoints in seconds to identify the earliest signs of developing problems. Probabilistic mathematics and adaptive statistical modeling provide highly accurate alerts.
Prelert's sophisticated analytics understand the normal behaviors and relationships hidden in your machine data. When a new issue emerges, the causal data has already been isolated. Mean time to diagnosis goes from hours to minutes.
Prelert is 100% self-learning. That means you point it at your data and get answers. No rules, thresholds or KPI. You spend less time coaxing information from your management data and more time building new capabilities.