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Comment by bogle | Hacker News Reader

Where I am at the moment, a UK bank's retail data analysis service, any ML projects are set up on a "learn as you go" basis whilst still doing your main job. No specialised practitioners are deemed required.

Whether or not this is a good idea is moot: this is in the bowels of the bank and not the bleeding edge of commerce.

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We don't really need specific ML practitioners for most jobs in the same way we don't need Genetic Algorithm practitioners in most jobs either, unless you're a researcher you can just read a book about it and start trying it out.

Indeed. If you already understand the data and the business and you already know Python or R then you can download Keras in the morning and have something useful to the business running in the afternoon. Then you can add ML to your CV.

It’s not clear where an ML expert who isn’t already familiar with the data and the business fits in or adds value, or even what an expert really means in industry.

Adding ML to your CV with the Mickey Mouse experience and understanding you describe would be a good way to get yourself drummed out of a job where you are actually expected to have the expertise you claim.

Agree with your point about domain knowledge being important.

Domain knowledge is really important, you can't overstate that.

But you've also succinctly described why a huge percentage of ML as practiced in industry under performs or flat out fails. To a first approximation the person who reads a few tutorials and plays with example data sets, then sets out to apply it to their own domain, has no real idea what they are doing - and it shows.

It is more like do you specialist x, eg if you have fullstack engineer do you really need frontend developer or DevOps engineer, generally no but there is case where specialist might make big differences.

fullstack engineer do you really need frontend developer

These terms are meaningless in both ML and finance.

I read it as an analogy, I think that's what was intended.

this seems deeply misguided.. the phrase "its all good" comes to mind, which is common, but meant to be comforting, not factual. This comment makes no distinction within the world of software, between architecture, experiment design, experiment interpretation, accurate input, accurate/effective team participation, reporting and communications, and more .. To day "read a book" then "do it" is a nice thought, certainly. The lack of self-awareness about what it is to build skill, and the lack of acuity to see what is effective practice versus say, noise.. hm

Perhaps, vetted and peer-reviewed on the one hand, and person on the commute bus with a pop-topic book on the other.. there has to be some awareness of that spectrum. With no indication of that awareness here, it just does not bode well.

You are talking about how you wish it was, but the comment you are replying to is merely stating how it actually is.

This comes down to how well your ML-leading exec is at articulating the business value of the project and coming up with a strategic roadmap to go from "learn as you go" to fully embedded while managing risk.

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