
Key Takeaways Feature quality determines model ceiling—no algorithm compensates for poorly engineered features that capture weak signal or introduce leakage. Training-serving skew causes 40% of production ML failures when feature computation logic diverges between offline training and online serving. Feature stores reduce skew by 70% but add…
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