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Why production systems keep making “correct” decisions that are no longer right [D]
Production machine learning systems can experience performance degradation where predictions appear reasonable and metrics don't change immediately, yet decision quality decays over time in ways diffi
Production machine learning systems can experience performance degradation where predictions appear reasonable and metrics don't change immediately, yet decision quality decays over time in ways difficult to detect without explicit monitoring. Most production ML failures stem from data and time problems rather than modeling issues, where systems make silent assumptions about how information arrives and changes if not explicitly designed. Data drifts quietly, business rules evolve, inputs arrive incomplete or late, and decisions suddenly carry financial or operational impact despite models maintaining their technical correctness.
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Source: r/MachineLearning | 2026-04-19