As with data science, the number of people that say they do "data science" and "machine learning" are in far greater supply than the number of opportunities.
Likewise, the number of people that are able to apply their domain knowledge to actually help their company's businesses/product or problems will always have work.
I've often used an analogy comparing this to cooking. Just because people say they know how to "cook" a dish, doesn't mean they'll be able to function or be effective in a restaurant or other type of business context kitchen. Yes, there's an element of this domain knowledge required, which are table stakes, but often the factors that distinguish success have more to do with understanding the context of the work and the surrounding business.
To update this for recent fads, learning to make sourdough bread does not mean one has learned how to run a bakery.
If you're able to be a pure researcher, or pure specialist, you're very lucky and working somewhere whey they can afford to have deep specializations.
The plethora of emerging tools and practices related to AI/ML "workflow" are related to the collective realization that training ML models is just one piece of the puzzle:
Likewise, the number of people that are able to apply their domain knowledge to actually help their company's businesses/product or problems will always have work.
I've often used an analogy comparing this to cooking. Just because people say they know how to "cook" a dish, doesn't mean they'll be able to function or be effective in a restaurant or other type of business context kitchen. Yes, there's an element of this domain knowledge required, which are table stakes, but often the factors that distinguish success have more to do with understanding the context of the work and the surrounding business.
To update this for recent fads, learning to make sourdough bread does not mean one has learned how to run a bakery.
If you're able to be a pure researcher, or pure specialist, you're very lucky and working somewhere whey they can afford to have deep specializations.
The plethora of emerging tools and practices related to AI/ML "workflow" are related to the collective realization that training ML models is just one piece of the puzzle:
https://papers.nips.cc/paper/5656-hidden-technical-debt-in-m... (See: Figure 1)