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It's Google; they have sufficient ML capacity to filter cases. It might require additional manpower in the short term, but it also lowers the likelihood of infr
by popinman322 7y ago
It's Google; they have sufficient ML capacity to filter cases. It might require additional manpower in the short term, but it also lowers the likelihood of infrastructure failure long-term.
What might not make sense, resource-wise, to smaller companies does start to make sense at their scale.
- jldugger 7y agoAnomaly detection on timeseries data is surprisingly bad. The data tends to be bad in all the wrong ways -- seasonal, and not normally distributed. And worse, you have no training data, so ML model you train simply learns whatever badness is allowed to persist in production as normal.
- cranekam 7y agoI worked at a FAANG and every now and then someone proposed or tried to make an anomaly detection system. They never worked well. It’s an extremely difficult thing to get right. Better to just have good monitoring of whether your system is responsive/available/reliable and ways to quickly work out why it’s not.