Déploiement

Ray Serve

Définition

An open-source, scalable model serving framework built on the Ray distributed computing library. Ray Serve supports complex inference pipelines with model composition, dynamic batching, and Python-native deployment, making it popular for LLM serving.

Termes Connexes

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.

Kubernetes for ML

Using the Kubernetes container orchestration platform to manage scalable, fault-tolerant AI/ML workloads in production. Kubernetes enables auto-scaling inference services, GPU resource management, and rolling model updates with minimal downtime.

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.

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