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Comment by golergka | Hacker News Reader

Fascinating; I just looked through the labs, and as a fullstack developer without that much experience in LLMs, it looks like I'm already closely familiar with half of them (git, flask, kafka, kubernetes) and the other half is just... code. No crazy math that I've come to associate with ML.

Does it mean that ML ops is a field that's actually not that hard to approach for a regular developer without a PhD?

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You can do a lot of work on MLOps and get very far without knowing much about ML. In a team with a senior ML engineers you are helping them scale and build stuff.

Like say you want to generate tons of synthetic data using simulations, you are likely to be more interested in questions say of batching, encoding formats, data loading etc than the actual process of generating unbiased data sets

If you need to collect and sample data from crowd sourcing, you likely need to know less about reservoir sampling than say figure out how to do it, online so it's fast or be efficient with $$$/compute spent on implementing the solution etc.

Reservoir sampling as in the stuff that's used in ReSTIR for graphics?

It's funny to me where statistics ends up sometimes.

Quite right. Its just software engineering with a fancy name. This work classification is only slightly better thought out than DevOps. In most companies ML engineers are engineers that understand software and some parts of ML and in best cases are good at both, in worst cases are terrible at both.

the PhD requirement is either for actual research positions or a gatekeeping function to help companies narrow down their massive candidate pools

I don't think "Ops" roles generally require a PhD.

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