Next-generation Alerting and Fault Detection

There is a common belief that in order to solve more [advanced] alerting cases and get more complete coverage, we need complex, often math-heavy solutions based on machine learning or stream processing.
This talk sets context and pro's/cons for such approaches, and provides anecdotal examples from the industry, nuancing the applicability of these methods.

We then explore how we can get dramatically better alerting, as well as make our lives a lot easier by optimizing workflow and machine-human interaction through an alerting IDE (exemplified by bosun), basic logic, basic math and metric metadata, even for solving complicated alerting problems such as detecting faults in seasonal timeseries data.

Dieter is an industrial engineer who started out as a systems engineer for the European social networking site Netlog, did some information retrieval/machine learning research at the university of Ghent, then joined Vimeo for backend/syseng stuff but ended up doing mostly open source monitoring and now works at raintank, the open source monitoring company behind Grafana.