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Pitfall #1 is a very good example where ML diverges from regular software development. You can't throw more cores, developers, time or fancy models at that prob
by MonkeyMalarky 4y ago
Pitfall #1 is a very good example where ML diverges from regular software development. You can't throw more cores, developers, time or fancy models at that problem. Sometimes, your data is going to limit what you can do and 80% accuracy just might be the best result you can get. Managers need to understand this and know when costs are sunk and when to move on.
- naijaboiler 4y agosometimes? 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.