Production Implementation at Scale

DATA INFRASTRUCTURE
Correlation analysis requires joining model metrics with business metrics. This is harder than it sounds. Model metrics are logged in real-time at millisecond granularity. Business metrics (purchases, subscriptions) may come from different systems with different schemas, retention policies, and identifiers.
Event stitching: Join model predictions to business outcomes using user ID, session ID, or request ID. Handle timing: a prediction at time T might lead to a purchase at time T+3 days. Join windows must be configured per business metric.
Attribution: When multiple model predictions precede a business outcome, how do you attribute credit? Last-touch attributes all credit to the final prediction. Multi-touch distributes credit across the funnel. Position-based gives more weight to first and last touches.
SEGMENTATION
Aggregate correlations hide important variation. The correlation between model AUC and revenue might be 0.3 overall, but 0.7 for power users and 0.1 for casual users. Segmenting reveals where model improvements matter most and enables targeted optimization.
Key segments: user tenure (new vs established), engagement level (high vs low), platform (mobile vs desktop), geography, and product category. Choose segments aligned with business priorities.
DASHBOARDS AND MONITORING
Real-time tracking: Display model metrics alongside business metrics. When model accuracy drops, does revenue follow? Lag-adjusted views account for delayed business metric updates.
Alerting: Alert when correlation breaks down. If historically model improvements led to revenue improvements but suddenly they diverge, something changed—new confounders, market shifts, or data quality issues.
Trend analysis: Track transfer functions over time. If the relationship between CTR and revenue weakens, investigate why. Saturation effects, competitive dynamics, or user behavior changes may explain drift.
✅ Best Practice: Build dashboards that show model and business metrics side-by-side with configurable lag windows. Enable drilling into segments to identify where correlations are strongest.

💡 Key Takeaways

✓Event stitching joins model predictions to business outcomes—requires user/session IDs and configurable join windows

✓Attribution models (last-touch, multi-touch) distribute credit when multiple predictions precede an outcome

✓Segmentation reveals where correlations are strongest—optimize for high-impact segments first

📌 Interview Tips

1Interview Tip: Explain event stitching challenges—different systems, schemas, and timing windows.

2Interview Tip: Describe why aggregate correlations can be misleading—segment to find true drivers.

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