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sometimes? all the time. if there is not enough signal in the data, there just isn't. Some people, and not management alone, including ML engineers, and DS th
by naijaboiler 4y ago
sometimes? all the time. if there is not enough signal in the data, there just isn't. Some people, and not management alone, including ML engineers, and DS think ML can do magic. It can't
- MonkeyMalarky 4y agoRight, signal in the data is always the limiting factor. It's just that sometimes you get the accuracy you need and you succeed with what you have and sometimes you don't, such is life. The worst is when management just can't let go of an idea and it becomes their white whale. The project I'm thinking of has gone on for 5 years now, been worked on by consultants and a few teams of data scientists that have come in through acquisitions and all quit. The only thing that's stayed constant is the shitty data, the dead horse and the management beating it hoping to get a better AUC.