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Scenario

Low-latency risk scoring at point-of-care where cloud connectivity may be intermittent. This case study demonstrates edge deployment patterns for healthcare applications that require:
  • Immediate response times for clinical decision support
  • Operation without network dependency in areas with poor connectivity
  • High reliability for patient safety-critical applications
  • Explainability for clinical acceptance and regulatory compliance
This scenario prioritizes reliability and explainability over model complexity, reflecting real-world healthcare deployment constraints.

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System Design

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Architecture Overview

The system implements local inference to eliminate network dependencies and ensure consistent latency:
1

Local CPU Inference

ONNX Runtime enables CPU-based inference directly on the edge device, avoiding cloud round-trips and network latency variability.
2

Conservative Thresholding

Threshold selection prioritizes minimizing false negatives in high-risk triage scenarios, accepting higher false positive rates when appropriate.
3

Drift Monitoring

Drift indicators are exposed through API endpoints for periodic model review and retraining triggers.

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Key Components

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ONNX Deployment

  • Runtime: ONNX Runtime for cross-platform CPU inference
  • Quantization: INT8 quantization reduces memory footprint and inference latency
  • Model Loading: Optimized initialization to minimize cold start times

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Edge Constraints

# Typical edge device constraints
Memory: 512MB - 2GB available for model + preprocessing
CPU: 2-4 cores, no GPU acceleration
Network: Intermittent 3G/4G or WiFi
Latency Target: < 100ms p99

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Threshold Strategy

Conservative threshold selection based on clinical risk tolerance:
  • High-risk triage: Lower threshold to catch more potential cases
  • Periodic calibration: Drift monitoring triggers threshold review
  • Fallback strategy: Manual review path for edge cases

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Trade-offs and Bottlenecks

Simpler models (linear, tree-based) are preferred over deep neural networks to improve:
  • Explainability: Feature importance visible to clinicians
  • Calibration stability: Less sensitivity to distribution shift
  • Resource efficiency: Lower memory and compute requirements
This trade-off accepts slightly lower accuracy for significantly improved reliability and clinical trust.
INT8 quantization provides substantial benefits:
  • Memory reduction: 4x smaller model size (FP32 → INT8)
  • Latency improvement: 2-3x faster inference on CPU
  • Accuracy impact: <1% accuracy loss on marginal predictions
Critical threshold decisions may shift slightly, requiring recalibration after quantization.
Initial inference latency is dominated by:
  • Model loading: 50-200ms depending on model size
  • Runtime initialization: 30-100ms for ONNX Runtime setup
  • First inference: Additional JIT compilation overhead
Mitigation strategies include model preloading at device startup and persistent runtime instances.

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Deployment Considerations

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Edge Infrastructure

Hardware Requirements

  • ARM or x86 CPU with 2+ cores
  • 512MB+ available RAM
  • Local storage for model artifacts
  • Battery life considerations for mobile devices

Software Stack

  • ONNX Runtime (CPU provider)
  • Lightweight HTTP server
  • Local preprocessing pipeline
  • Drift detection module

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Connectivity Patterns

Online Mode (when connected):
  • Upload prediction logs for monitoring
  • Download model updates
  • Sync drift metrics to central platform
Offline Mode (intermittent connectivity):
  • Fully functional local inference
  • Queue telemetry for later upload
  • Fallback to last synced model version

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Assumptions and Limitations

Critical AssumptionsThese limitations must be validated before deployment:
  1. Input Schema Stability
    • Feature schema is stable and validated upstream
    • Missing values handled by preprocessing layer
    • Type validation before inference
  2. Memory Availability
    • Edge nodes have sufficient memory for model plus preprocessing overhead
    • Memory profiling performed under peak load conditions
    • Swap/paging disabled for deterministic latency
  3. Model Scope
    • Model trained on population representative of deployment context
    • Regular retraining cycle to address distribution drift
    • Clear escalation path when model confidence is low
  4. Regulatory Compliance
    • Clinical validation performed before deployment
    • Audit trail maintained for all predictions
    • Human-in-the-loop for final clinical decisions

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Implementation References

This case study leverages repository components from:
  • ONNX Export: workflows/onnx_deployment.py for model conversion
  • Quantization: workflows/onnx_deployment.py INT8 quantization pipeline
  • Drift Detection: ml_pipelines/data_quality_checks.py for monitoring
  • API Endpoints: Standard prediction API with drift indicator exposure
Review the ONNX deployment workflow to see concrete implementation of quantization and edge optimization techniques.

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