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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)
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