Production Implementation Architecture and Cost Optimization

Building production prediction drift monitoring at scale requires careful system design to balance detection latency, statistical power, compute cost, and operational complexity. The architecture must handle billions of predictions daily across thousands of models while keeping alerting latency under 15 minutes and infrastructure costs reasonable.

The data flow starts by logging every prediction with timestamp, model version, key slice dimensions like country and device, and sampled probability or score. Use sampling rates of 1 to 10 percent to reduce volume by 10 to 100 times while maintaining statistical power. Send logs to a streaming system like Kafka or batch store like S3. Build per model, per slice, per window aggregations of prediction distributions using stream processing frameworks or scheduled batch jobs. The aggregation layer is critical for cost control. Store fixed width or equi-depth histograms with 50 to 200 bins rather than raw predictions. For regression with heavy tails, use quantile sketches like t-digest to efficiently track percentiles. This reduces storage by 100 to 1000 times. For 100 models with 200 slices each and 100 bin histograms, you maintain 20 thousand histograms totaling under 100 megabytes in memory.

For windowing, use overlapping short windows like 5 minutes sliding every minute for fast detection, and require confirmation from longer 1 hour windows before paging. Maintain rolling baselines over 7 to 30 days and seasonal baselines for same hour-of-day comparisons, storing at least 4 to 8 weeks of baseline histograms to cover holidays and special events. Compute divergence metrics like JS divergence or KS test on histogram pairs in under 200 milliseconds per slice. With 20 thousand slices and 1 minute cadence, total CPU usage per model stays under 1 core. Apply hierarchical alerting to manage false positives and rate limit to one alert per slice per hour to reduce pager fatigue.

💡 Key Takeaways

•Use 1 to 10 percent sampling on raw predictions to reduce ingestion volume by 10 to 100 times while maintaining statistical power for drift detection with 5 to 15 minute windows on 100 thousand sampled events

•Histogram aggregation with 50 to 200 bins reduces storage by 100 to 1000 times compared to raw predictions. For 100 models with 200 slices each, maintain 20 thousand histograms totaling under 100 megabytes in memory

•Overlapping windows provide fast detection with confirmation: 5 minute sliding windows every minute for rapid alerts, require confirmation from 1 hour window before paging to reduce false positive rate by 10x

•Maintain multiple baseline types with retention policies: 7 to 30 day rolling baselines, seasonal baselines storing 168 hourly histograms for hour-of-day patterns, and 4 to 8 weeks retention to cover holidays and special events

•At scale of 20 thousand histogram comparisons per minute (100 models × 200 slices), JS divergence computation takes under 200 milliseconds per slice, keeping total CPU under 1 core per model with efficient histogram data structures

📌 Examples

Major streaming platform ingests 10 million predictions per minute at 1 percent sampling rate, builds 100 bin histograms per 5 minute window across 200 slices per model, completes divergence computation in under 200 milliseconds per slice on single CPU core, achieving 15 minute end to end alerting latency

Ads platform with 1 billion predictions per day stores only 100 bin histograms per model per hour, reducing storage from 100 gigabytes raw logs to 100 megabytes aggregated histograms, saving 99.9 percent on storage costs while maintaining drift detection accuracy

Credit risk system runs weekly batch monitoring on 10 million applications using PSI on pre-aggregated score bins, completes full run in under 30 minutes on 10 CPU cores, generates compliance reports retained for 7 years at under 1 gigabyte per year

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