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The article considers the issue of choosing a machine learning method for solving the applied problem of assessing the current state of the quality of IT services. As a method of choice, a comparative analysis of the generally accepted methods of machine learning was carried out using a set of criteria that made it possible to evaluate their effectiveness and efficiency. F-measure is considered as the main criteria, as a generalizing concept of the completeness and accuracy of classification, for each class of states separately and the duration of the training and prediction procedure. All operations were carried out on the same dataset, namely, on the data of the centralized monitoring and management system for the IT infrastructure of Russian Railways in terms of the ETRAN IT service. Due to their heterogeneity and taking into account the practice of applying the Barlow hypothesis, the initial data went through preliminary processing, the algorithm of which is also described in detail in the article.

Various instrumental IT infrastructure monitoring systems are considered and compared: Zabbix, Nagios, ManageEngine OpManager, Hewlett Packard Operations Manager, Naumen Network Manager and IBM Tivoli. The functions to be performed by the IT infrastructure monitoring and management system in its target state are specified. The current state of the IT infrastructure monitoring system in JSC Russian Railways is described. A mathematical formulation of the problem of determining control values of metrics and an example of developing a neural network to determine control values of metrics and recommendations for its improvement are given.