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Fraud Detection & Anomaly Detection • Real-time Scoring (Low-latency Inference)Medium⏱️ ~3 min

Accuracy vs Latency Trade-offs: Model Cascades and Dynamic Batching

The Fundamental Trade-off
More accurate models are typically slower. A deep neural network achieves higher precision but takes 50ms; a linear model runs in 1ms with lower precision. The business decides: is 2% higher precision worth 50x latency increase? For fraud detection, missing a fraud case costs more than a few milliseconds—but only up to the point where latency causes checkout abandonment.
Model Cascade: Use cheap fast models to filter easy cases, expensive accurate models only for ambiguous ones. If the fast model is 95% confident either way, skip the slow model entirely. This reduces average latency while maintaining accuracy where it matters.
Cascade Architecture
Stage 1 (1ms): Rules engine and blocklist checks. Stage 2 (5ms): Lightweight gradient boosted model on core features. Stage 3 (30ms): Deep neural network with full feature set. Each stage decides: pass, block, or escalate. Only 10-20% of transactions reach stage 3, cutting average inference time by 80%.
Dynamic Batching
GPUs process batches efficiently—32 requests together run in 40ms total versus 640ms individually (20x speedup). Dynamic batching collects incoming requests, waits until batch fills or timeout (5-10ms), then processes together. Trade-off: batching adds latency for the first request in the batch.
Batching Insight: Set batch timeout based on P99 latency budget. If budget is 50ms and model inference is 30ms, allow 10-15ms for batching. Under low traffic, requests may wait the full timeout; under high traffic, batches fill quickly.
Model Distillation
Train a small fast student model to mimic a large accurate teacher model. The student achieves 90-95% of teacher accuracy at 10x speed. Use the student for real-time serving, the teacher for offline analysis and labeling.

💡 Key Takeaways

✓Model cascades filter easy cases with fast models, running expensive models only on the 10-20% ambiguous cases—80% latency reduction

✓Dynamic batching provides 20x GPU throughput improvement but adds latency for first request in batch

✓Knowledge distillation trains fast student models achieving 90-95% teacher accuracy at 10x speed

📌 Interview Tips

1Describe a cascade: Stage 1 rules (1ms), Stage 2 lightweight model (5ms), Stage 3 deep model (30ms)—only ambiguous cases escalate

2Set batch timeout based on P99 budget: if budget is 50ms and inference is 30ms, allow 10-15ms batching window

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