Best Practices for Edge AI
Learn how to optimize your edge AI deployments for performance, reliability, and efficiency.
Model Optimization
Quantization
Quantize your models to reduce size and improve inference speed:
- INT8 Quantization: 4x smaller, minimal accuracy loss
- FP16 Quantization: 2x smaller, no accuracy loss (GPU)
- Dynamic Quantization: Runtime optimization
Model Pruning
Remove unnecessary weights:
- Reduces model size
- Speeds up inference
- Maintains accuracy
Architecture Selection
Choose the right model for your device:
- Nano/Small: Low-power devices, real-time applications
- Medium: Balanced performance and accuracy
- Large: High accuracy, more powerful devices
Resource Management
CPU Optimization
- Use multi-threading for batch inference
- Set appropriate CPU limits
- Monitor CPU usage and adjust
Memory Management
- Monitor memory usage
- Set appropriate memory limits
- Use memory-efficient frameworks
GPU Utilization
- Enable GPU when available
- Batch inference for efficiency
- Monitor GPU temperature
Deployment Strategies
Blue-Green Deployments
- Deploy new version alongside old
- Switch traffic when ready
- Rollback if issues occur
Canary Deployments
- Deploy to subset of devices first
- Monitor performance
- Gradually roll out to all devices
Health Checks
- Implement health check endpoints
- Monitor response times
- Set up automatic restarts
Monitoring and Logging
Metrics to Track
- Inference latency
- Throughput (requests/second)
- Error rates
- Resource utilization
- Model accuracy (if applicable)
Logging Best Practices
- Use structured logging
- Include request IDs
- Log errors with context
- Set appropriate log levels
Security
Model Security
- Encrypt model files
- Use secure model storage
- Validate model integrity
API Security
- Use authentication tokens
- Implement rate limiting
- Validate input data
- Sanitize outputs
Performance Tuning
Batch Processing
Process multiple inputs together:
- Reduces overhead
- Improves throughput
- Better GPU utilization
Caching
Cache frequently used results:
- Reduce computation
- Faster response times
- Lower resource usage
Async Processing
Use asynchronous inference:
- Non-blocking requests
- Better resource utilization
- Improved user experience
Troubleshooting
Common Issues
- High Latency: Check model size, input preprocessing
- Memory Errors: Reduce batch size, optimize model
- Low Accuracy: Verify input format, check model version
- Device Disconnects: Check network, review logs
Continuous Improvement
- Monitor performance metrics
- A/B test model versions
- Collect feedback
- Iterate and improve