There are lots of things wrong with "Machine Learning" practice that are problems of the practice instead of the subject itself.
One of the most dangerous things that comes out of academia is the "project orientation", that is, the idea that something is "done" at a certain point in time.
The "technical debt" aspect comes in when you are managing a product or a program, which can be thought of as a series of projects that influence each other.
The "paradigm" is that somebody wants to learn machine learning so they download the same set of digits that Hinton used in his papers, and then they replicate the result, and then they announce they are done. When you see everybody is chasing the same problems (Hinton's digits, the Kaggle of the day) you start to realize that the setup of a problem which can be solved by machine learning is actually worth more than the learning.