The field of system observability has been greatly enhanced by the application of eBPF. eBPF generates data at critical points in the execution of a system and that data is used for observation via software like Sysdig and Cilium. I propose to utilize the data generated for system state clustering. This is an application of machine learning to the above data to understand if the system is behaving properly or not.
The amalgamation of machine learning and system data generation in real-time would open the doors to a plethora of applications like system state prediction, preventive replacement of system components aided by ML. This talk will take the attendees through an idea of how this could be done.
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