[visarga]

Yes, I've come to the same conclusion. When interviewing ML engineers, I prefer to know they are exceptional programmers with passable knowledge of ML than the other way around. If they haven't learned to be good software engineers it's improbable they will in the future, but ML can be learned. In fact a ML team needs a large number of regular software engineers, there's a lot of non-ML code to work on, such as labelling interfaces, data pipelines and CI/CD for models.

[logicslave12]

Real ML and not just plug and play models takes a serious amount of knowledge

[RussianCow]

On the other hand, unless ML is your core competency as a business, plug and play models can get you really far.

[PaulHoule]

When you consider the chain from inputs to action you can go far with a small toolbox (e.g. logistic regresssion.)

Plug-and-Play models in visual recognition basically work at this point in time; in NLP they don't.

[eanzenberg]

I wouldn’t have ML engineers doing ML. They should be working on scaffolding, maintenance, production side, etc. The worst ML scientists (producing ML models) I’ve experienced were software developers who transitioned.

[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.

[nimithryn]

Curious why you think the worst ML scientists were the former engineers (as someone who does a bit of both).

[jrib]

> ML can be learned

Curious: do you have a recommended pathway for that?