I don't agree, most of the low hanging fruit in ML engineering hasn't been picked yet. ML is like electricity 100 years ago, it will only expand and eat the world. And the research is not slowing down, on the contrary, it advances by leaps and bounds.
The problem is that we don't have enough ML engineers and many who go by this title are not really capable of doing the job. We're just coming into decent tools and hardware, and many applications are still limited by hardware which itself is being reinvented every 2 years.
Take just one single subfield - CV - it has applications in manufacturing, health, education, commerce, photography, agriculture, robotics, assisting blind persons, ... basically everywhere. It empowers new projects and amplifies automation.
With the advent of pre-trained neural nets every new task can be 10x or 100x easier. We don't need as many labels anymore, it works much better now.
The problem is that we don't have enough ML engineers and many who go by this title are not really capable of doing the job. We're just coming into decent tools and hardware, and many applications are still limited by hardware which itself is being reinvented every 2 years.
Take just one single subfield - CV - it has applications in manufacturing, health, education, commerce, photography, agriculture, robotics, assisting blind persons, ... basically everywhere. It empowers new projects and amplifies automation.
With the advent of pre-trained neural nets every new task can be 10x or 100x easier. We don't need as many labels anymore, it works much better now.