I would say this is also true in academia if working on applied problems. For example, ML applications in healthcare: a lot more beyond a robust analysis, including privacy issues, human subjects, knowledge of regulations, deployment ecosystem, etc.
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Rule 33 is simply wrong and introduce info-leakage from test to train for non iid data.
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Very true. Also my (albeit limited) experience. It's not magic, it's still software and software engineering is hard. Secure software even harder.
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Marking it for later
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We are going through this right now and I agree 100. Figuring out what data you have and logging more is one of the first steps as well as how to store it.
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Loved rule #1
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Resonates so much
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So true
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So true man ,
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