Capability
Model Serving With Kserve For Inference With Traffic Splitting And Canary Deployments
20 artifacts provide this capability.
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KubeflowPlatform50/100
ML toolkit for Kubernetes — pipelines, notebooks, training, serving, feature store.
Unique: Abstracts framework-specific serving runtimes (TensorFlow Serving, TorchServe, Triton) behind a unified InferenceService CRD, enabling users to deploy models without learning framework-specific serving configuration. Supports traffic splitting and canary deployments natively via Kubernetes service mesh integration.
vs others: More portable than cloud serving (SageMaker, Vertex AI) because it runs on any Kubernetes cluster; more flexible than framework-specific serving (TensorFlow Serving alone) because it supports multiple frameworks with unified interface.