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Comment by deepsun | Hacker News Reader

I've dived into ML (and DL) with 17 years of software development experience. I'd say it's much easier than software. Yes, there's A TON to learn and experiment with, but still much less than with software. I was able to feel confident enough after just 1.5 years learning and kaggling, and passed easily ML interviews to SF Bay Area companies (hint -- all data science people are extremely glad to see software experience, much more than data science).

"Good pipeline and bad model is much better than bad pipeline and good model" (c) someone

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Bad pipeline means bad data, and bad data means bad model. Its as simple as that.

Bad pipeline is too strong. But good enough pipeline + good enough models > bad pipeline + great model

My wife is a researcher at Stanford doing some ML stuff and the constant painpoint isn't the researching of novel models or maths or whatever, it's the IT hell of managing a pipeline and data acquisition. I've helped her introduce things like Docker to the lab which seems to have helped but still - software engineering is a totally different skill that really hinders ML work.

Beyond what makes a good model, IME at a FAANG building an ML product, the bulk of the work in practice tends to be general software engineering. There need to be a sufficient number of people who understand the actual ML pieces under the hood, but even when you're making changes to the models, the bulk of the actual work is not complex ideation, but implementing the ideas in software, and this implementation is usually something you can learn in a few months.

Can you share how you switched into ML? How and what did you study? Any tips?

Yeah, would appreciate if parent answers this.

I imagine he is doing the tooling to support the ML team. I've seen listings for that type of work that don't require any ML experience. Usually you need a PhD if you want to be developing and tuning models.

For English-speakers -- Kaggle forums. Top kaggler (Giba) said he learned everything just reading Kaggle forums (he was an electric engineer).

I personally mostly talked to friends, Kaggle, and 1-2 Coursera classes.

PS: Kaggle is pretty far from real production ML, but good enough to dive in.

>said he learned everything just reading Kaggle forums (he was an electric engineer)

The math you learn when studying electrical engineering plus all the signal processing stuff, gives you a good foundation to pivot to ML.

It's a shame electrical engineering is so poorly paid by default in comparison to SW dev.

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