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Completely agree. The vast majority of the failed projects fail at the planning stage. To put it simply, if the cost of a misprediction is high then it is usu
by iknownthing 2y ago
Completely agree. The vast majority of the failed projects fail at the planning stage. To put it simply, if the cost of a misprediction is high then it is usually not going to work because all models make mispredictions. This is something that can be identified in the planning stage i.e. it's usually completely avoidable. Also, of the 20% of projects that succeed, I wonder how many of them actually needed ML.
- steveBK123 2y ago> The vast majority of the failed projects fail at the planning stage. This is my experience in 20 years of SWE as well. The biggest project failure debacles were obvious from the get-go to all the senior ICs on the team. Generally managers were pushing them for "reasons", and in some cases even wink-wink about the fact they too didn't believe in the project but.. "reasons".
- pseudosavant 2y agoThis is my experience too. I find that most "regular" people think 95% is basically 100%, and not "it fails 1 out of 20 times consistently". Even 99% isn't good enough in many cases and should not be considering infallible.
- Yizahi 2y agoAlso in a case when misprediction is cheap, but makes the whole system unrealiable in such mode of work. E.g. my company tries (too late) to ride the hype and created a halfbaked tool for internal use to classify test results. Since the accuracy of neural networks is never 100% it simply doesn't matter as it doesn't save any time, all test results needs to be verified manually anyway. But several people are busy full time working on it, reporting some results, some amazing performance metrics and so on. And are very visibly upset when we push back or plainly say that the tool is worthless. :)