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From my experience, this is pretty common in AI Internal tools used by a dozen or a couple dozen AI engineers/Data Scientists, the models they output used by m
by shmatt 4y ago
From my experience, this is pretty common in AI
Internal tools used by a dozen or a couple dozen AI engineers/Data Scientists, the models they output used by millions. It's totally worth keeping 5-10 person engineering team to create better and stronger tools that make upgrading and deploying new models easier and faster
- time_to_smile 4y ago> totally worth keeping 5-10 person engineering team I've worked in AI/ML for a long time, and I can tell you that in all but the rarest of exceptions nobody has sat down and done the math to really determine if that value is there. In my experience, most cases it's not. I've seen teams like you describe and then asked them "what's the difference in value between what our model does today and the theoretical optimum"? The answer, which nobody liked, was fractions of a penny per user. That means if the team achieved perfection it wouldn't really matter in practice. We're going to see a lot of the AI/ML teams disappear as companies are forced to focus on determining the real value add being provided.