Kubernetes for ML: KServe and Seldon Core

Serving a single model behind a container is straightforward, but running many models for many teams, with autoscaling, rolling updates, canary traffic, GPU sharing, and self-healing, demands an orchestration platform, and Kubernetes has become that platform for production ML. Yet raw Kubernetes is general-purpose and low-level; expressing a model deployment in plain Kubernetes objects means hand-writing deployments, services, autoscalers, and ingress for every model. KServe and Seldon Core exist to close this gap: they are model-serving frameworks built on Kubernetes that add ML-specific abstractions, so you declare an inference service and they generate the underlying machinery, scaling, routing, canary, explainability hooks, monitoring integration. The problem they solve is the enormous boilerplate and missing ML features of bare Kubernetes serving: without them, every team reinvents autoscaling and traffic-splitting per model in verbose YAML. KServe and Seldon let you describe the model and the serving intent at a high level and inherit production-grade serving behaviour, turning Kubernetes from a powerful but raw substrate into a managed-feeling model-serving platform that scales across an organisation's whole fleet of models.