2 ms·
This describes 99% of ML projects in industry. There is a reason the incentives are such that a science team creates a solution and then a theoretical engineer
by lumost 6y ago
This describes 99% of ML projects in industry. There is a reason the incentives are such that a science team creates a solution and then a theoretical engineering team deploys it. The Science team gets credit for the analysis/prototype but the prototype is so delayed getting to production that only the engineers take the blame or the scientists have moved on.
- joe_the_user 6y agoThis describes 99% of ML projects in industry. This may well be true given how fragile a construct most deep learning systems are. But there are still substantially different deployment issues for different systems. Google can deploy a question-answerer or a search-by-image-description system and lowered accuracy in practice doesn't really do much damage. Google can't deploy a self-driving car with low safety in the same way.