Traditional monitoring techniques may no longer be able to handle the complexity of modern applications, infrastructures and environments. These do not make the best use of the massive amounts of data being generated, thus several alarms are created that are not necessarily indicative of a new incident. The main objective of this thesis is to improve the monitoring and alarm generation by applying different Machine Learning algorithms and techniques with the rich and vast amount of data, to accurately detect complex problems even if they are outside the boundaries of the monitored software, which is common in modern architectures such as the Micro Service. The proposed work is framed within a critical IT application inside an international organization, in order to provide business and research value by solving a real world modern problem. The case study in question, consists in developing a monitoring solution using state of the art production Machine Learning (ML) algorithms, based on the modern Artificial Intelligence for IT Operations (AIOps) Platforms, to detect anomalies and generate reliable alarms for complex faults in HERMES, a critical application of EDP.