if you work in applied ml, let the project scope or expected near future project scopes dictate the locus of your learning. learn the unchanging principles (how to diagnose errors, and what to do about that) and then ride the wave instead of trying to drink it. if this is too vague please ask me more
Edit: i think the scientists (researchers) are more in trouble than the engineers (which i what i call an applied ML practitioner)