Can confirm. Data science team just schooled the entire engineering department in our Deep Racer tournament.
Machine learning is still too hard for software engineers
11–20 of 154 posts
Re: Machine learning is still too hard for software engineers
#12Earlier quoted context omitted.
In my experience, both are true. I'm more on the ML side, and I can tell I don't have the kind of routine and habits that good software engineers have, though I'm learning. But on the other hand, and I've seen this from software engineers who've made the transition to ML, and clearly have a good handle on the concepts (in one case even published papers in ML journals), they don't seem to have the intuition that allow…
I think the above commenter is more critiquing the notion of relying on intuition all together.
Right, I see. That's not really possible imo. For things like mlops, sure. But model development, selection, evaluation? From what I've seen, it's exactly when engineers reach for standard tools without giving thought to how it applies to the given problem that they do a bad job.
Re: Machine learning is still too hard for software engineers
#13[deleted]
Re: Machine learning is still too hard for software engineers
#14I'd argue software engineering is still too hard for ML engineers. Most of ML (not research but commercial applications of what's already been demonstrated) is now well within the realm of engineering, but there are few standard practices, bodies of knowledge, or agreed on processes for doing anything. These are problems that engineering formalism solve, not another auto-ml tool. Maybe I'm saying the same thing as th…
To add to your point, the Hidden Technical Debt in Machine Learning Systems paper: https://proceedings.neurips.cc/paper/2015/file/86df7dcfd896f...
Re: Machine learning is still too hard for software engineers
#15I really like the premise, and I really agree with it, but I don't think a SaaS is a solution, the solution is trying to find better abstractions that makes things simple and easier for the developers, using code, and without limiting their flexibility, but that's the hardest thing to do! As an experienced software developer who used to learn a new framework every week, I thought ML was going to be piece of cake. In…
Good points. But truth is SaaS can be a great solution if you pick Nyckel!
Sorry, it had to be done.
Re: Machine learning is still too hard for software engineers
#16Wow, it's almost like machine learning and software engineering are different disciplines entirely!
Just because both involve coding doesn't mean software engineers should be expected to have the math chops (stats/prob, linalg, calc, etc.) to make machine learning work for them...
Vice versa is a little more complicated, because ML/DS can be done very inefficiently without the proper coding practices, but understanding the math is independent of that so that point still holds for this comparison.
Re: Machine learning is still too hard for software engineers
#17The best tutorials for indoctrinating software engineers in ML are written accessibly & use real-world business-case data, or easily comprehended data sets. One great example of this, from graph theory, was this fantastic 2013 article by Kieran Healy: https://kieranhealy.org/blog/archives/2013/06/09/using-metad...
There's no getting around the complexities of fit, bias and customized models for many ML problems, so my observation above is obviously limited in its applicability.
Re: Machine learning is still too hard for software engineers
#18Another perspective, someone with no programming experience whatsoever today can in 1 minute create a new ML application for natural language tasks with performance that would blow most SOTA systems from 5 years ago out of the water, using things like the openai API. And this trend will almost certainly continue, where many tasks can be programmed by simply asking/describing the problem to a massive model and letting it work magic.
Re: Machine learning is still too hard for software engineers
#19I'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
Re: Machine learning is still too hard for software engineers
#20I'm ok with this. ML should remain expensive and hard to use while we grasp how to regulate it and understand it. We need less ML, not more of it.