This usually means that the kernel is using more memory than available either for the container configuration or allowed via cgroup if running locally. You can get a better idea of the reason for the failure if you can execute the same code in a python recipe instead of a notebook and check the output log.
I recommend also checking your running background tasks by going to Administration > Monitoring > Running background tasks and unload any tasks that may have been left running. Note, you must be an admin to do this.
If you continue to receive this error, please email or live chat support, and attach the instance diagnostic (Administration > Maintenance > Instance diagnosis).