I love the photos of equations. "We use enhanced Bayes' Rule and Joint Summation algorithms to serve you the most relevant and useful dashboards." Probably more truth to it than it seems :)
Glad you liked it :) It turns out that modeling operational metrics is a lot harder than I expected, so there was quite a bit of work we had to do to get the algos to work.
I was wondering about this. I assume you're treating this info as proprietary, but in case you aren't I'd love to hear more about how you actually implement this.
Well, there's a lot of details and I don't think I can cover it all here, but if you're interested in the general framework that I used to approach this problem, take a look at this blog post I wrote last year: https://medium.com/@upal/how-to-use-machine-learning-to-debu.... Let me know if you have any feedback!
I am a little surprised that you got some milage out of procedures and algorithms that rely on the Gaussian assumption (that includes k-means). From experience, the raw metrics are, shall we say, as violently non-Gaussian as it gets. OK OK you need not tell what you are doing to address that as I myself am being rather economical with the truth. But so glad, so glad that someone is using multivariate analysis, about time too. From someone who dabbles in a similar space I wish you well.
Thank you :) Yes, the use of multivariate analysis was a crucial insight for me, and I'm hoping these ideas will push the monitoring community forward!