but more seriously, unless someone's explicitly doing ML research for most applications using something off-the-shelf-ish[0] and tinkering with it works best. and this mostly requires direct experience[1] with the stack.
and sure, of course, if said project/team/org/corp has so much money they even can train their own model, sure, they can then afford to have these separate roles with "more dedicated" domain experts.
[0] from YOLO to LLaMa to whatever's now on HuggingFace
[1] the more direct the better. you have used LLMs before? great. pyTorch? great. you can deploy stuff on k8s and played with ChatGPT? well, okay, that's ... also great. you know how to get stuff from Snowflake/Databricks/SQL to some training job? take my money!
but more seriously, unless someone's explicitly doing ML research for most applications using something off-the-shelf-ish[0] and tinkering with it works best. and this mostly requires direct experience[1] with the stack.
and sure, of course, if said project/team/org/corp has so much money they even can train their own model, sure, they can then afford to have these separate roles with "more dedicated" domain experts.
[0] from YOLO to LLaMa to whatever's now on HuggingFace
[1] the more direct the better. you have used LLMs before? great. pyTorch? great. you can deploy stuff on k8s and played with ChatGPT? well, okay, that's ... also great. you know how to get stuff from Snowflake/Databricks/SQL to some training job? take my money!