KFServing

KFServing is an open-source framework designed to enable serving machine learning models on Kubernetes. It provides a standardized way to deploy, manage, and scale inference services across multiple frameworks and runtime environments, with emphasis on low-latency predictions and containerized deployment. By integrating with Kubernetes and Knative, KFServing aims to simplify production model hosting and enable automated scaling based on demand.

Origins and evolution: KFServing originated within the Kubeflow ecosystem to address the need for a common, framework-agnostic model serving layer on Kubernetes. Over time the project was rebranded and evolved into KServe, which continues to provide the same core capabilities under a broader governance model and with expanded community support. The KFServing/KServe approach is widely used to standardize deployment patterns for machine learning models in cloud-native environments.

Architecture and key concepts: The primary user-facing resource is the InferenceService Custom Resource. A typical InferenceService defines a predictor specifying a runtime and model storage location, along with optional transformers for pre- or post-processing. Supported runtimes include TensorFlow Serving, TorchServe, NVIDIA Triton Inference Server, ONNX Runtime, Scikit-Learn, and others. KFServing leverages Knative for autoscaling, enabling scale-to-zero behavior when there is no traffic and rapid scaling when demand increases. It also supports versioning and deployment strategies that facilitate rolling out new model revisions.

Usage and scope: KFServing/KServe targets production-grade model serving on Kubernetes clusters, aiming to streamline deployment pipelines, multi-framework support, and operational control for data science and ML engineering teams. It is commonly used within Kubeflow deployments and various Kubernetes-based ML workflows.

See also: Kubeflow, Kubernetes, KServe, model serving, inference.