>>SV_Bub+22
Not really, things fail in unexpected ways. Automated anomaly detection is notoriously error prone, leading to a lot of false positive and false negatives, in the trivial case of monitoring a single timeseries. For a system the size of GitHub, you need to monitor a whole host of things and if it's quasi impossible to do one timeseries well, there's basically no hope of doing automated many timeseries anomaly detection with a signal-to-noise ratio that's better than "humans looking at the thing and realizing it's not going well".
There's stuff like this that can't be automated well. The automated result is far worse than the human-based alternative.