Déploiement

Edge AI

Définition

Running AI models on edge devices—industrial controllers, cameras, vehicles, mobile phones—rather than in the cloud. Edge AI reduces latency, preserves data privacy, and enables operation in environments with limited connectivity, making it critical for physical AI deployments.

Termes Connexes

Inference

The process of running a trained model on new data to produce predictions or generated outputs. Inference cost and latency are the dominant operational concerns in production AI, particularly for large generative models that can cost cents per request at scale.

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.

Physical AI

AI systems that perceive and act in the physical world through robotics, autonomous vehicles, smart manufacturing equipment, and industrial sensors. Physical AI integrates computer vision, reinforcement learning, and real-time control to automate tasks previously requiring human dexterity and judgment in unstructured environments.

Services Connexes

Product Leadership Program

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