Cost, Scale, and Trade-off Analysis

COMPUTE COST
Drift detection adds overhead. For each monitored feature, you compute statistics on current data and compare to baseline. With 500 features and hourly monitoring, that is 500 × 24 = 12,000 drift computations per day.
Cost drivers: number of features monitored, window size (larger windows = more data to process), statistical test complexity (PSI is fast; embedding comparisons are slow), and monitoring frequency.
Optimization: prioritize high-impact features. Not all features need equal monitoring. Monitor critical features hourly, secondary features daily, low-impact features weekly. Use sampling to reduce data volume.
STORAGE COST
Drift detection requires storing historical distributions. Options: store raw data (expensive, flexible), store aggregates only (cheap, limited analysis), or store sketches (space-efficient approximations).
Data sketches like T-Digest (for percentiles) and Count-Min Sketch (for frequencies) reduce storage 10-100x while preserving statistical properties. Trade-off: some precision loss in exchange for massive cost reduction.
SCALE CONSIDERATIONS
At scale (millions of requests, thousands of features), drift monitoring becomes a significant infrastructure component. Consider: dedicated compute resources, partitioned storage, sampling strategies, and tiered monitoring (critical vs secondary features).
Scaling pattern: centralize drift computation as a platform service rather than embedding in each model pipeline. Shared infrastructure amortizes cost and ensures consistency.
ROI ANALYSIS
Drift detection costs resources but catches problems early. A model degrading undetected for 2 weeks might cost $1M in lost revenue. If drift detection costs $50K/year and catches one such event, it pays for itself 20x.
Track: drift alerts issued, true positives (drift confirmed, action taken), false positives (alert ignored), and missed drift (problems found by other means). Use these to justify and optimize monitoring investment.
💡 Key Insight: Drift detection ROI is asymmetric. Cost is predictable (infrastructure); benefit is preventing rare but expensive failures. Invest in monitoring proportional to the cost of missed drift.

💡 Key Takeaways

✓Compute cost: features × frequency × test complexity; prioritize critical features, use tiered monitoring frequency

✓Storage: data sketches (T-Digest, Count-Min) reduce storage 10-100x with minor precision loss

✓ROI is asymmetric: predictable monitoring cost vs rare but expensive undetected drift events

📌 Interview Tips

1Interview Tip: Explain tiered monitoring—hourly for critical features, weekly for low-impact.

2Interview Tip: Justify drift detection investment with ROI calculation: cost of monitoring vs cost of missed drift.

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