Machine learning is still too hard for software engineers
1–10 of 154 posts
Re: Machine learning is still too hard for software engineers
#2I'm doing ML a long time, practice makes perfect etc. If anything is too hard, it is front end.
Re: Machine learning is still too hard for software engineers
#3I 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 reality, I went down this rabbit hole 4 years ago, and I'm still in there, I was so innocent back then
Re: Machine learning is still too hard for software engineers
#4I'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 the article, just framed differently?
Re: Machine learning is still too hard for software engineers
#5So is software engineering
Re: Machine learning is still too hard for software engineers
#6I'm doing ML a long time, practice makes perfect etc. If anything is too hard, it is front end.
Yeah, it felt like a sales person being confused why can't become an expert fullstack software engineer on up to date modern stacks without a lot of effort and time.
Re: Machine learning is still too hard for software engineers
#7I'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…
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 allows them to select the right tool for the job, or understand when a given technique is appropriate; you could call that "modeling maturity" - a combination of mathematical maturity and the skill of relating domain knowledge to modeling choices.
Re: Machine learning is still too hard for software engineers
#8I'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…
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.
Re: Machine learning is still too hard for software engineers
#9One might as well write an article claiming that "UI design is still too hard for software engineers" or "controlling a nuclear power plant is still too hard for software engineers" -- which are true (and it is equally true that software engineering is hard for UI designers and nuclear power plant operators).
Who came up with this silly idea that something that is a valid knowledge domain of its own is suddenly going to become "easy"?
Re: Machine learning is still too hard for software engineers
#10Missing in these advertisements-disguised-as-blogs is an estimate of effort or time. Let's say ML is critical to a new product or internal tool, how many man-years is it reasonable to invest? If your expectation is using ML like a drag-and-drop app builder then you're probably best off using a canned tool but then you won't really have any competitive advantage.