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> If you have to choose between engineering and ML, choose engineering. It’s easier for great engineers to pick up ML knowledge, but it’s a lot harder for ML ex

> If you have to choose between engineering and ML, choose engineering. It’s easier for great engineers to pick up ML knowledge, but it’s a lot harder for ML experts to become great engineers. If you become an engineer who builds great tools for ML, I’d forever be in your debt. Is this true, or at least the majority view? Maybe it is because the comparison is unbalanced, because "great engineer" is on both sides but the ML part is "pick up ML knowledge" and "ML experts". I'd choose engineering because the employment opportunities are so much more favourable, not because it's easy to 'pick up ML knowledge'. I'm in the beginning of many hours of mathematics and statistics study so that I can become competent at ML, and in comparison to say, becoming good at git and python and AWS, this feels harder.

grenoire 6y ago
I do think it's true, and likely the majority view too. The saying has many, many alternatives (for quantitative finance, it's "you can teach a math PhD finance but you can't teach a finance PhD math [at the same level that a math PhD is at]") in different studies. I think it just boils down to knowing the fundamentals or not, and arguably a person who only studied ML will lack a wide range of tools in their toolkit to be able to tackle tangential problems that an engineer could (whereas the engineer already has the tools to build up their ML toolkit). In my anecdotal experience with teaching, knowing how to learn is heavily correlated with how general (as opposed to focused) the field the person is in.
cdavid 6y ago
I agree 100 %, and I started as a PhD in ML 15 years ago. DL, thanks to its "lego aspect", is getting ML commoditized fast for most applications out there. It hollows the gap between applying more or less directly existing models and pure research. There is a glut of engineers who think ML engineering is playing with TF or pytorch all day, retraining models on well defined datasets. The reality: you want to use computer vision to do something cool server side ? Just using resnet/etc. and fine tuning on your data, maybe using as an embedded space, will get you 90 & there. What's hard ? Identifying opportunities, and especially data. I know it is cliché, but identifying what kind of problems you can solve w/ the data you have, and being able to convince the business side that what you do is useful, all of that is very hard. Same for the ability to solve 'big problems' while delivering regular deliverable so that you're not seen as a cost center. How do you link model improvements to business impact ? The system side of it is still immature as well. I say that to all my reports, directs or indirects: hone your SWE skills, make sure you understand how you bring value to your company. Unless you're in the top 1 %, you're not going to survive with ML skills alone.

grenoire 6y ago

I do think it's true, and likely the majority view too. The saying has many, many alternatives (for quantitative finance, it's "you can teach a math PhD finance but you can't teach a finance PhD math [at the same level that a math PhD is at]") in different studies. I think it just boils down to knowing the fundamentals or not, and arguably a person who only studied ML will lack a wide range of tools in their toolkit to be able to tackle tangential problems that an engineer could (whereas the engineer already has the tools to build up their ML toolkit). In my anecdotal experience with teaching, knowing how to learn is heavily correlated with how general (as opposed to focused) the field the person is in.

cdavid 6y ago

I agree 100 %, and I started as a PhD in ML 15 years ago. DL, thanks to its "lego aspect", is getting ML commoditized fast for most applications out there. It hollows the gap between applying more or less directly existing models and pure research. There is a glut of engineers who think ML engineering is playing with TF or pytorch all day, retraining models on well defined datasets. The reality: you want to use computer vision to do something cool server side ? Just using resnet/etc. and fine tuning on your data, maybe using as an embedded space, will get you 90 & there. What's hard ? Identifying opportunities, and especially data. I know it is cliché, but identifying what kind of problems you can solve w/ the data you have, and being able to convince the business side that what you do is useful, all of that is very hard. Same for the ability to solve 'big problems' while delivering regular deliverable so that you're not seen as a cost center. How do you link model improvements to business impact ? The system side of it is still immature as well. I say that to all my reports, directs or indirects: hone your SWE skills, make sure you understand how you bring value to your company. Unless you're in the top 1 %, you're not going to survive with ML skills alone.