Déploiement

ONNX (Open Neural Network Exchange)

Définition

An open format for representing machine learning models that enables interoperability across frameworks and hardware. Exporting to ONNX allows models trained in PyTorch to run efficiently on TensorRT, OpenVINO, or other optimised runtimes.

Termes Connexes

Triton Inference Server

NVIDIA's open-source inference serving software that supports multiple frameworks (TensorRT, ONNX, PyTorch, TensorFlow) on GPU infrastructure. Triton is widely used in enterprise deployments requiring maximum throughput from GPU hardware.

Model Compression

A set of techniques—including quantization, distillation, pruning, and low-rank factorisation—that reduce model size and computational requirements while preserving performance. Model compression is essential for deploying powerful models on edge hardware or within cost budgets.

Model Serving

The process of deploying trained ML models to production environments where they can receive inputs and return predictions at scale. Model serving infrastructure must address throughput, latency, versioning, and cost while meeting SLAs.

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