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Machine learning is still too hard for software engineers
101–110 of 154 posts
Re: Machine learning is still too hard for software engineers
#102I'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…
Most folks don't realize that the vast majority of ML applications can be done with preexisting models, you don't need someone with a PhD to fine tune it unless you need to eek out a tiny bit of improvement. The real value is in the person preparing the data for training.
Re: Machine learning is still too hard for software engineers
#103I'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 experien…
Can you share how you switched into ML? How and what did you study? Any tips?
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.
Re: Machine learning is still too hard for software engineers
#104Most CS grads coming onto the engineer market now will have ML exposure through their chosen college courses. As this wave of knowledge makes its way through the industry, the value of ML specialist knowledge will decline, especially as off-the-shelf pre-trained models improve. Very few companies then will be able to justify the luxury of a dedicated in-house data science/ML engineering team. In other words, most sof…
> in the same way most software engineers today are Docker/cloud proficient engineers
oh shit, are we all supposed to be now?
Re: Machine learning is still too hard for software engineers
#105Look it’s just buzzwords all the way down. There’s only a handful of people who can do this stuff. {Your business} will drop some buzzwords, someone will write an if statement, management high fives all around and fat bonuses. Deal with it.
Re: Machine learning is still too hard for software engineers
#106I'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…
Most folks don't realize that the vast majority of ML applications can be done with preexisting models, you don't need someone with a PhD to fine tune it unless you need to eek out a tiny bit of improvement. The real value is in the person preparing the data for training.
This is not true at all unless you're only referring to commodity applications. There's no preexisting model for general regression or seq to seq, for instance. You can say, oh, that's a good fit for an LSTM, but you still need to tune the individual layers to suit the application. Although I am slightly conflating ML and DL, since you mention training which heavily implies DL.
Re: Machine learning is still too hard for software engineers
#107I'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…
Ha, I agree that software engineering is too hard for ML engineers, and even for software engineers like myself who have been doing it for 20 years like
zcw100 said :).
Author of the blog post here. It was definitely written from my narrow viewpoint and experience. Our goal is to make more solutions accessible to software developers and instinct was the same as yours - a lot of ML can be within the realm of engineering (even small / one-person teams) and that there are accidental complexities standing in the way of wider use. Our solution (AutoML+SaaS) def doesn't work for every situation. I'm curious to hear more of your thoughts on how ML can be made more accessible to Eng (and vice versa).
Re: Machine learning is still too hard for software engineers
#108No post body was provided.
Re: Machine learning is still too hard for software engineers
#109> basic like image classification
Yes, it is going to be laborious to have state-of-the-art deep learning implemented in your infrastructure.
Re: Machine learning is still too hard for software engineers
#110It seems to me the biggest challenge for SE transitioning into ML is that ML is a very broad topic and people conflate a lot of roles together. From purely research based questions (backbones, optimizers, initializers etc), to more 'MLOps' like pipelining questions, which tend to fall into the classical engineering / dev ops buckets. So the real question is what type of ML do you want to do?
If you're looking to land a job at FAIR / Deepmind or Google Brain/ Nvidia Research as a researcher or ML scientist the expectations of knowledge are very different than 'data science'. These are research lab groups, that work on pushing the state of the art forward. They are also supported by great engineers, building awesome tools that improve ML research. So transitioning into this sort of role requires more than doing Kaggle competitions, it requires developing an intuition for the respective ML subfield / and trying new things and usually failing. i.e. this is a research role and will require a lot of study and learning
If on the other hand you are looking for datascience / take model and build pipeline to run AI, or perform hyper param sweeps or simply modify some model code, then on I would say that is much more engineering than research ML. This has a much lower barrier to entry coming from engineering and could be a good stepping stone to a transition into pure ML research.
On a more general note to consider when thinking of transitioning to ML is that these systems are probabilistic in nature vs purely deterministic as they are in more general software systems. People (ie humans) are bad at wrapping their heads around distributional processes - you can see this in all fields that deal with them (Quantum vs Classical Physics, Biological Systems etc).
In general I guess what I have seen is when engineers try to dip their toes into ML, what's required is a mindset shift in how to approach problems. Once that happens the depth of that shift determines the type of role with ML you wish to pursue.