I think you need a bit more competence to get into the training realm though, because it's a bigger step to create a new model - especially the hard step of data labeling.
Unless you have a novel data set and a way to quickly train you're probably better off using existing trained models in most cases.
I agree with the transfer learning piece wholeheartedly though.
Data labeling isn't hard, it's labor intensive, which is an entirely different resource. If the business goal is valuable enough, then a non-tech manager without any special expertise can organize twenty man-months of grunts to do the labeling, three man-months of cookie-cutter junior dev work for tools of labeling and data management, and a single man-month of an external consultant with proper expertise to write sensible guidelines on how the labeling should be done and supervise the process. All of which will cost something comparable to a the annual cost a single ML developer.
Training models often is tricky, but it's not that hard, my experience shows that decent undergrads learn to train standard models on their own datasets after a single one semester course, and train quite difficult models after two semesters; so teaching/learning basic ML takes comparable time and effort to e.g. teaching/learning basic JS frontend development.
So if some company's IT department has some minimum ML skills, lack of expertise shouldn't be preventing them from training models. And even more so, using your own data (IMHO) is the whole point of adopting ML; if the problem is so generic that you don't need to adapt it to your data, then you shouldn't be learning to use ML but rather buying and integrating a SaaS API run by someone else.
Which is a form of hard...for example if you need 60,000 semantically labeled images, you need to train people to know how to do that specific of labeling and then have them do it, then QC the data, break it up into training and validation sets etc...
Don't forget that this advice is for a front end dev who hasn't ever touched caffe or torch or whatever. In many cases it takes new people a week to set up drivers and an environment on a GPU.
Unless you have a novel data set and a way to quickly train you're probably better off using existing trained models in most cases.
I agree with the transfer learning piece wholeheartedly though.