It's interesting that you mention this, because there's quite an impressive resurgence of privately funded R&D going on in the ML space. We're in an interesting

It's interesting that you mention this, because there's quite an impressive resurgence of privately funded R&D going on in the ML space. We're in an interesting phase, where the field is moving too fast to have 'canonical' methodologies (though we're getting close). To be an engineer in the deep learning space often requires reading and keeping up with research. Everything just gets dumped on arXiv, because the peer-reviewed publication cycle is almost too slow for the field. Making a successful transition to ML in my opinion, depends a lot on the individual. Without a strong background in calculus, linear algebra and statistics, it's going to be difficult. Training a model is what people tend to focus on, but in my opinion, that's the easy part. Evaluating/validating a model, analyzing and preparing your data, anticipating model performance, understanding what to do to improve your fit, model selection or architecture. Developing custom deep learning architectures at times requires a bit of an abstract mathematical intuition that I think will suit many engineers very well. A lot of engineers are well equipped to be successful in making a transition, but on the other hand, at least as many aren't. In the future, I think the field will have many varying degrees of expertise, with the barriers to entry becoming lower all the time. We're reaching a point where some common use cases can be solved adequately in a nearly automated fashion. Some "autoML" tools don't really require any real understanding of ML, though I think it's not wise to get in the habit of using them without understanding how to evaluate a fit. These tools will be great for people who want to occasionally use ML to solve some smaller problems, but as a part of their larger job function. In some middle area, ML engineers and practitioners will be training and operationalizing models, and keeping up with major developments in research. But there will be some significant changes in the next decade. I predict the nebulous of data science, data analysis and machine learning will become formalized into 3 major skills - exploratory data analysis, machine learning and advanced computational statistics. At the lowest level, researchers will continue developing the field, which like you say, is probably not something you transition directly into.