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.
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.
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.