3 ms·
> if you're talking about building anything, that is already too hard for ML researchers. I don't think so. I agree it is too hard for the ML researches at th
by ezekiel68 2y ago
> if you're talking about building anything, that is already too hard for ML researchers.
I don't think so. I agree it is too hard for the ML researches at the companies which will have their rear ends handed to them by the other companies whose ML researchers can be bothered to follow a blog post and prompt ChatGPT to resolve error messages.
- jokethrowaway 2y agoa lot of ML researchers stay pretty high level and reinstall conda when things stop working and rightly so, they have more complicated issues to tackle It's on developers to provide better infrastructure and solve these challenges
- currymj 2y agoI'm not really talking about companies here for the most part, I'm talking about academic ML researchers (or industry researchers whose role is primarily academic-style research). In companies there is more incentive for good software engineering practices. I'm also speaking from personal experience: I once had to hand-write my own CUDA kernels (on official NVIDIA cards, not even this weird translation layer): it was useful and I figured it out, but everything was constantly breaking at first. It was a drag on productivity and more importantly, it made it too difficult for other people to run my code (which means they are less likely to cite my work).