Hi, I'm Ines Panker.

When you deploy some code, do you know its impact on your production stats, like memory usage, request duration, error rates?

If you wanted to know if your code improved or deteriorated the speed of SQL queries, would you know where to get this data?

In this talk, I walk through a real-world algorithm I built to automatically detect pattern changes to metrics: no thresholds set by hand, and no LLMs.

I’m not a data scientist (I don’t even have an ML degree), which meant my first attempts were… not great. I threw some off-the-shelf ML libraries at the problem, hoping they’d just work. Which, of course, they didn’t.

I had no choice but to actually sit down, understand what production time series data really looks like, and then reach for simpler statistical tools. It turns out they work better than anything off-the-shelf, once you figure out how to apply them.

I’ll walk through the evolution of the algorithm, the edge cases I uncovered, and the tricks I’ve set up. The system ran on real customer data from 2020. It worked great, spotted real degradations, and yet… it kinda never got many fans. So it will soon be archived. Still, it was the most fun problem I ever got to work on: no library to install, no right answer to copy, just data and trial and error.