Searching 20 GB/sec: Systems Engineering Before Algorithms
This comes up in my work modestly frequently, generally with a slightly different scenario for the tradeoff between "complicated and considered" versus "cheap and dirty but gets the job done."<p>e.g. We could spend 3 weeks using feature vectors and backtesting against prior data to figure out what signals accounts which are likely to churn send (for the purpose of proactively identifying them and attempting to derisk them, like by having the customer success team help them out directly)... or we could write a two-line if statement based on peoples' intuitive understandings of what unsuccessful accounts look like.  (No login in 30 days and doesn't have the $STICKY_FEATURE enabled?  Churn risk!)<p>The chief benefit of the second approach is that it actually ships, 100% of the time, whereas priorities change and an emergency develops and suddenly 75% complete analysis gets thrown in a git repo and forgotten about.  Actually shipping is a very useful feature to have.