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Model is cheap, data is HARD. Plus investing is data is much more predictable: the outcome is always going to get better, though the margin will be diminishing
by karmasimida 8y ago
Model is cheap, data is HARD.
Plus investing is data is much more predictable: the outcome is always going to get better, though the margin will be diminishing, but better is better.
While modeling is not, hiring 100 machine learning 'experts' will not solve the problem 100 times better than 10 of them. On the other side, 100 labelers are surely going to provide 10 times of throughput provided the same upscale.
- aqme28 8y ago> though the margin will be diminishing So by the metric of model performance (the only thing that matters here), what you're saying is that hiring more labelers is actually not linear.
- karmasimida 8y agoTrue, of course. We can only go as high as 100% for accuracy, right? However the labellers are scalable in terms of their throughput and coverage of the data, you can always find bad examples or holes in your current data plane, and that is the time when labelers, not the scientists, are going to rescue you