Detection of Anomalies
One of the many available machine learning approaches is anomaly detection. Inconsistencies in a series of numbers or other data are detected with this approach. Data points in a data set that don’t seem to fit the expected pattern are called anomalies.
When a value deviates significantly from the norm, this can be regarded as a data alarm. Anomaly Detection enables early detection and avoidance of potentially dangerous activities. In practice, this could be, for example, detecting gadgets that consume more energy than normal, or unusual traffic on a website, or events that point to upcoming application issues.
Pattern recognition using machine learning.
In a nutshell, anomaly detection is the ability to discover patterns of repetition, time, and deviation. And because this kind of detection is self-learning, its accuracy increases over time, allowing users to extract more and more relevant information from it.
Tuuring collects, normalizes, and analyzes massive amounts of data before converting it into meaningful performance metrics, providing a holistic view of the entire workload stack. Tuuring’s machine learning engine has native anomaly detection and artificial intelligence, enabling the following possibilities;
1 – Create an AI-based performance foundation for historical, real-time, and forecast data.
Tuuring creates performance baselines from data sets to generate historical insights, real-time trends, outlier analysis, and future performance forecasts using an integrated machine learning engine. Dashboards can be customized to display analytical data and outcomes that can be shared and broadcast to a range of devices.
2 – Improve the efficiency of time-consuming application chains.
Based on changes in metrics, baselines, and analysis, alerts can be produced and presented within the program and/or passed on to third-party ITSM applications. This allows faster problem resolution and better root cause analysis.
3 – Combination of data from apps, workplaces, and (cloud) infrastructures.
Tuuring collects important performance data from a number of sources for apps, workplaces, and (cloud) infrastructures. This data is bundled in the Tuuring data program, which makes it possible to, by using artificial intelligence, detect anomalies in the entire chain instead of just a part of it.
Tuuring collects data from a wide variety of performance data sources to break down the IT silos. Collected information focuses on everything related to performance, including platforms and solutions for users, applications, workspaces, and (cloud) infrastructure. This includes both generic and proprietary APM solutions and business-related data, such as real transactions and orders, to perform anomaly detection analysis and optimization.
Curious about the possibilities of Tuuring? Request a non-binding demonstration and discover how anomaly detection can bring value to your business.