Graduated on University of Zagreb on Faculty of Organization and Informatics, Information Systems, 2008. After graduation Marko started working at Poslovna inteligencija where he gained extensive experience in the field of data warehousing systems, data integration, business intelligence and Big Data analytics on numerous national and international projects. Today, Marko is Director of Innovation and Development department at the same company, where he is responsible for research and introduction of new technologies and the development of Big Data applications. He also participates in complex projects of data integration and advanced analytics in the role of system architect, lead consultant and project manager.
Complex, fast-moving datasets make it nearly impossible to spot infrastructure problems, intruders, or business issues as they happen using rules or humans looking at dashboards. Elastic machine learning features automatically model the behavior of your Elasticsearch data — trends, periodicity, and more — in real time to identify issues faster, streamline root cause analysis, and reduce false positives.
With machine learning, users can go deeper and ask questions like “Have any of my services changed behaviour?” or “Are there any unusual processes running on my hosts?” These questions require behavioural models of hosts or services that can be automatically built from data using machine learning techniques. Elastic machine learning features make all that possible and apply to a broad range of use cases and datasets, allowing users to get creative with where and how you use them.