AI2FUTURE2019 –2 days, 4 keynotes, 20+ moving presentation
10th & 11th of October 2019, Kraš Auditorium, Zagreb, Croatia
AISoft Technology
Miroslav Popovic
CEO and Co-funder
Miroslav Popović is a CEO and Cofunder at AISoft Technology. Miroslav holds a PhD degree in computer science. His interests include big data systems, real-time stream processing, and AI.
He has had prior experience in researching new user interaction paradigms and engineering experience in Google on a statistical machine translation project. Among other industrial projects in his career he has worked on weather forecasting systems and as an Android engineer.
Mislav’s
Idea
AISoft Technology is developing innovative framework for building smart real-time monitoring and AI solutions for delivering seamless observability of big data systems, which in the end has a goal of reaching autonomous system operations and maintenance.
The hart of observability in big data systems is a powerful autonomous root cause analysis (aRCA) component. It is known that the problem of RCA does not have a single dominant solution which performs with optimal results over a range of application domains. Instead, a wide range of approaches can be applied and RCA solutions are often customized to a specific domain. We demonstrate how our machine learning approach is applied to fine-tune the parameters of dependency graph which is used in RCA. We also demonstrate early alarming scenarios upon detecting health problems in the core system infrastructure which streams data through Kafka. Within our framework we strive for a better user experience with the big data world, therefore we experiment with new methods of interaction based on virtual reality that utilize AI technologies, such as Leap Motion and natural speech chat bot.
The hart of observability in big data systems is a powerful autonomous root cause analysis (aRCA) component. It is known that the problem of RCA does not have a single dominant solution which performs with optimal results over a range of application domains. Instead, a wide range of approaches can be applied and RCA solutions are often customized to a specific domain. We demonstrate how our machine learning approach is applied to fine-tune the parameters of dependency graph which is used in RCA. We also demonstrate early alarming scenarios upon detecting health problems in the core system infrastructure which streams data through Kafka. Within our framework we strive for a better user experience with the big data world, therefore we experiment with new methods of interaction based on virtual reality that utilize AI technologies, such as Leap Motion and natural speech chat bot.
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