Production Architecture and Implementation Patterns

DATA COLLECTION LAYER
Production drift monitoring starts with logging. Log every prediction request with features and timestamps. Store in a queryable format (columnar stores like Parquet work well for analytical queries).
Sampling considerations: for high-QPS systems (100K+ requests/second), logging everything is expensive. Sample 1-10% of traffic. Ensure sampling is stratified across segments (geography, user type) to catch segment-specific drift.
Feature storage: compute and store summary statistics (mean, std, percentiles, histograms) at regular intervals. Raw feature storage enables ad-hoc analysis but costs more. Most systems store aggregates plus a sample of raw records.
COMPUTE ARCHITECTURE
Batch processing: Run drift detection as daily/hourly batch jobs. Simple to implement. Latency is window size plus processing time. Good for most use cases.
Streaming processing: Compute drift statistics in real-time using stream processing (Kafka + Flink). Detects drift within minutes. Higher infrastructure complexity. Use for latency-critical applications.
Hybrid: stream processing for critical features, batch for comprehensive analysis.
ALERTING AND DASHBOARDS
Threshold-based alerts: Alert when PSI > 0.25 or K-S p-value < 0.01. Requires tuning thresholds per feature based on historical variability.
Anomaly-based alerts: Train a model on historical drift metrics. Alert when current drift is anomalous given historical patterns. Adapts to expected variation.
Dashboard essentials: per-feature drift over time, top drifting features ranked by magnitude, comparison of current vs baseline distributions. Enable drill-down from aggregate to segment level.
✅ Best Practice: Start with batch processing and threshold-based alerts. Add streaming and anomaly detection as you scale. Simple systems with good thresholds catch most issues.

💡 Key Takeaways

✓Sample 1-10% of high-QPS traffic; stratify by segment to catch segment-specific drift; store aggregates + raw sample

✓Batch processing for most cases; streaming for latency-critical (<minute detection); hybrid for critical features

✓Threshold alerts (PSI > 0.25) need per-feature tuning; anomaly-based alerts adapt to historical patterns

📌 Interview Tips

1Interview Tip: Describe the data pipeline: logging → sampling → aggregation → drift computation → alerting.

2Interview Tip: Explain when streaming drift detection is worth the complexity vs batch processing.

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