I can only talk from my own experience but there's other way than just straight up go to ML/DL and crank out new models. That way is to come from the applicatio

I can only talk from my own experience but there's other way than just straight up go to ML/DL and crank out new models. That way is to come from the application domain side. [0] For me that was robotics, with the motivation that traditional method felt like it wouldn't scale outside static environments so I started to look in to ML/DL (Deep Reinforcement Learning really) and from the looks of it I'm not alone. [0] Now I do research in it, without a PhD nor taking any courses in it at my masters. (except one DL course where we had to code everything including gradient flow from scratch. No framework) Frankly, going pure DL at research level today seems like a steep uphill; the top labs and research institute(including industries) are the ones that are producing (and notably training) most of the SOTA models. Getting in to those circles are your best bet but then a PhD at a top university under a top professor is the best bet, and competition to get in to those are insane [0] https://www.natolambert.com/writing/path-into-ai https://www.natolambert.com/writing/path-into-ai