[visarga]

You just need one ML scientist for 4-5 software (or ML) engineers. If you wand to optimise time to delivery of products, you have much more to gain by improving the software engineering part, because regular SWE it's 90% of the product.

One of the main differences between ML in academia and industry is related to sourcing the training data. In academia they just use available pre-tagged datasets such as ImageNet, in industry you have to collect, clean up, organise, train, iterate with new data.

[MisterPea]

Yep, you pretty much hit the nail on the head. A few people in this thread are equating ML scientist's responsibilities to those of an ML engineer, however, they are very different.

Production development of a model is a very hard problem and it's interesting because I see few companies trying to tackle it. One of them is tecton.ai (heard about them on SED) and I'll be interested to see how they evolve their feature set because it still seems incomplete.

[PaulHoule]

Wow! This is the elephant in the room that doesn't get talked about in that article at all. You're also the only person to mention it, despite the fact that it is the only essential step to a commercial machine learning project.