I'd at least debate if it's much harder to learn a new web framework and it's concepts or whatever is required to solve the ML tasks at a company. If you know h

I'd at least debate if it's much harder to learn a new web framework and it's concepts or whatever is required to solve the ML tasks at a company. If you know how database+frontend+backend work (and are already used to HTML/CSS/SQL//JS+another language), you can also on the job learn a new framework. Knowing the library is the least hard part about ML work just like knowing the web framework is the least hard part about webdev (both imo). It's much more important to understand the actual problem domain and data and get a smooth data pipeline up and running. Scaling, optimizing inference, squeezing out better performance and annoying labeling. There's a pretty solid gap from applying some framework to a preexisting and never changing dataset vs. curating said dataset in a changing environment. And if we're talking about RL and not just supervised/unsupervised then building a suitable training environment etc. also become quite interesting. If someone asked me "what's so hard about webdev" my answer would be similar btw...it's fairly easy to set up a reasonably complicated "hello world" project in any given framework but it gets a lot harder when real world issues like different auth worklflows, security, scaling and handling database migrations etc. enter the picture.