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Google Cloud Certified Professional Machine Learning Engineer · Google Cloud
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24-hour window is too infrequent for proactively preventing drift's negative effects.
Option B configuration represents the ideal balance for a high-stakes LTV model in a high-traffic environment: WHY 1. 10% sampling rate is highly cost-effective while providing more than enough data for statistically sound drift detection. 2. 24-hour monitoring window provides a stable, reliable signal by smoothing out normal diurnal variations in user behaviour, thus minimising false positives and alert fatigue.
Subscription LTV data doesn’t change rapidly → Hourly checks (D) are unnecessary. Monitoring 10% of data per day (B) is sufficient → Detects drift while minimizing cost. Cost consideration → Hourly monitoring (D) increases expenses without significant added value for slow-changing data.