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Also, the general lack of scientific rigor in the field is more often than not coming from people with little engineering experience. If anything, knowing how to deal with system complexity makes your more rigorous (in the sense of the scientific method)
Buggy code is bad science. Poorly tuned benchmarks are bad science. Poorly factored code is bad science (hinders reproducibility, increases chances of a mistake). If your field is all about empirical validation, then your code *is* a large part of your scientific output.
Similarly, deployment of ML systems is not a completely independent branch with a one-way connection to research. Remember that the purpose of machine learning research is to *generate knowledge about how to build ML systems that work in the real world*.
In the same way that the physical reality we can measure is the ultimate referential for physics research, the real-world effectiveness of our algorithms and systems is the ultimate referential for machine learning research.
Machine learning is not mathematics (if it were, we would call it mathematics)
Buggy and incomplete code is too common in the ML research field due to fast prototyping and weak support we need a better plan for Open research source
Tell this to the Applied Electrophysics community which likely still relies on distributed systems with gnarly AF Fortran 77 code. I think the good science == food code rule applies to ML research, but I could show you some good science backed by some code that you make you run.
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