Photo by Ivan F on Unsplash

Photo by Ivan F on Unsplash

Artificial intelligence improves monitoring

Voyage of Discovery

Article from ADMIN 57/2020
Partially autonomous monitoring systems – or at least intelligent alerts – have the potential to reduce significantly the workload of administrators and the service desk.

Artificial intelligence (AI) and machine learning are on the rise in IT, especially in the field of system monitoring. AI, machine learning, and deep Learning often are wrongly used as synonyms. AI is the ability of a machine to make decisions similar to those made by humans. For example, software could decide to trigger an alarm that a human being would also have triggered. AI is the simulation of intelligent behavior by more or less complex algorithms.

Machine learning, on the other hand, means classifying methods, procedures, and algorithms that help the machine make decisions. Machine learning is the math that lets AI learn from experience. From this perspective, machine learning merely provides the basis for decision making.

An example can illustrate this point. The learning result is a percentage that assigns 90 percent of the current data to a certain type. Whether the machine then considers this value together with others as the trigger for an alarm has nothing to do – mathematically speaking – with the part of the algorithm that calculates the value.

Machine learning generally works more reliably the more data you have available. This method can also be understood as a filter. Today, most companies have such an abundance of data that manual evaluation is inconceivable. As a remedy, machine learning algorithms and other methods can be deployed to filter the available data and reduce it to a level that allows interpretation. After preparing the data set appropriately, rules for intelligent software behavior can then be defined.

In traditional machine learning, the user decides as early as the implementation stage which algorithm to use or how to filter what set of information. In deep learning, on the other hand, a neural network determines which information it passes on and how this information is weighted. Deep learning methods require a great deal of computing power. Although the underlying math has existed for a long time,

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