You can spend a lot of time making a great solution...
... for the wrong problem.
It kind of happens all the time; also in machine learning! So often that it is kind of an open door. But maybe that's a great reason to talk about it. In this talk I'll share some stories of data/code problems there were solved twice: once for the wrong problem and once for the right one.
This is a talk on failures of solutions: natural, artificial and "intelligent". It'll be a list of stories. Some of them will include:
My goal is to show that in order to solve a data problem, it might be good to take a step back once in a while and to try to see the bigger picture.