Production Implementation Architecture and Cost Optimization

DATA COLLECTION ARCHITECTURE
Log predictions with metadata: timestamp, model version, features used, prediction score, predicted class (if applicable). Store in a queryable format for historical analysis.
Sampling: For high-QPS systems, sample 1-10% of predictions. Ensure stratified sampling by key dimensions (user type, product category) to maintain segment representativeness.
Storage: Time-series databases work well for prediction monitoring. Columnar formats (Parquet) enable efficient historical queries. Retain 30-90 days of detailed data; longer for compliance needs.
COMPUTE PIPELINE
Batch processing: Run drift detection as hourly or daily batch jobs. Simple, cost-effective. Detection latency equals batch interval.
Streaming processing: Compute drift metrics in real-time using stream processing frameworks. Sub-minute detection. Higher infrastructure cost.
Typical pattern: Streaming for critical metrics (prediction mean, class distribution). Batch for comprehensive analysis (full distribution comparison, slice-level monitoring).
COST OPTIMIZATION
Reduce sample size: Statistical significance with 10,000 samples is similar to 100,000 samples. Sample aggressively for cost savings.
Aggregate before compare: Compute histograms or sketches instead of storing raw predictions. Compare aggregates rather than individual values.
Tiered monitoring: Critical models get real-time monitoring. Less critical models get daily batch monitoring. Match monitoring intensity to business impact.
ALERTING INTEGRATION
Feed drift metrics into centralized alerting (PagerDuty, Opsgenie, or internal systems). Set severity levels: critical (immediate page), warning (investigate within hours), informational (review in daily standup).
✅ Best Practice: Start with batch processing on sampled data. Add streaming only when batch latency becomes a measurable business problem. Premature optimization wastes engineering effort.

💡 Key Takeaways

✓Log predictions with metadata; sample 1-10% stratified by key dimensions; retain 30-90 days

✓Batch for comprehensive analysis, streaming for critical metrics; match monitoring intensity to business impact

✓Cost optimization: aggressive sampling, aggregate before compare (histograms/sketches), tiered monitoring by criticality

📌 Interview Tips

1Interview Tip: Describe the tradeoff between batch and streaming processing for drift monitoring.

2Interview Tip: Explain cost optimization: sampling, aggregation, tiered monitoring based on model criticality.

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