Déploiement

Kubernetes for ML

Définition

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.

Termes Connexes

Containerization (AI)

Packaging AI models and their dependencies into portable, isolated containers (Docker) for consistent deployment across environments. Containerization eliminates "works on my machine" problems and is the foundation of reproducible, auditable AI production systems.

Ray Serve

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.

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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