The normalized data is suitable to be processed through machine learning algorithms to automatically reduce noise and identify the probable root cause of incidents. The main output of such stage is the detection of any abnormal behavior from users, devices or applications.

Noise reduction can be done by various methods, but most of the research in the field points to the following actions:

1. Analysis of all incoming alerts;
2. Remove duplicates;
3. Identify the false positives;
4. Early anomaly, fault and failure (AFF) detection and analysis.^[14]

Anomaly detection - another step in any AIOps process is based on the analysis of past behavior of users, equipment and applications. Anything that strays from that behavior baseline is considered unusual and flagged as abnormal.

Root cause determination is usually done by passing incoming alerts through algorithms that take into consideration correlated events as well as topology dependencies. The algorithms on which AI are basing their functioning can be influenced directly, essentially by "training" them.^[15]

A very important use of AIOps platforms is related to the analysis of large and unconnected datasets, such as the Johns Hopkins Covid-19's data published through GitHub.^[16] The data in this example is pulled from a large number of un-normalized databases - aggregated data (10 sources), US regional data (113 sources) and Non-US data (37 sources), which are unuseable considering the needed emergency response time by the traditional analysis models.

Generally, the main areas of use for AIOps platforms and principles are^[17]

• Automation of tasks (DevOps)
• Machine learning platforms
• Augmented reality
• Agent-based simulations
• Internet of things (IoT)
• AI Optimized Hardware
• Natural language generation
• Streaming data platforms
• Conversational BI and analytics

• Deployment and integration testing^[18]
• System configuration^[18]
• Service quality monitoring and anomaly detection^[18]
• Resource scheduling and optimization^[18]
• Capacity/workload management and prediction^[18]
• Hardware/software failure prediction^[18]
• Auto-diagnosis and problem localization^[18]
• Incident management^[18]
• Auto service healing^[18]
• Data center management^[18]
• Customer support^[18]
• Security^[18]
• Privacy^[18]