Learn ONNX with Real Code Examples

ONNX Runtime can outperform native frameworks for inference

Quantization reduces model size and improves latency

Graph optimizations improve throughput

GPU and accelerator support is available for high-performance deployment

Batching inputs increases inference efficiency

Validate model inputs to prevent inference attacks

Use secure storage for exported ONNX models

Ensure runtime environment is trusted

Follow enterprise data governance for deployment

Monitor inference pipelines for anomalies

Log inference latency and throughput

Monitor GPU/CPU utilization

Track batch performance

Visualize model predictions vs expected outputs

Audit model deployment pipelines

Document model export steps

Validate inference against training framework outputs

Maintain versioned ONNX models

Use automated tests for inference consistency

Monitor runtime performance and logs