Introduction
KServe is a Kubernetes-native, open-source model serving framework built to provide high-performance, standards-based deployment of ML models in production. Formerly known as KFServing, KServe is part of the Kubeflow ecosystem and designed to support multi-framework, multi-tenant, and production-grade model serving. KServe enables data science and ML teams to deploy and manage models with minimal operational overhead while maintaining consistency, scalability, and observability across all environments.
Key benefits of using KServe include:
Multi-Framework Model Support: Natively supports TensorFlow, PyTorch, XGBoost, scikit-learn, and custom model servers—enabling standardized deployment regardless of framework.
Serverless Inference: Leverages Kubernetes and Knative to automatically scale model endpoints up and down based on traffic, optimizing cost and responsiveness.
Production-Ready Features: Offers built-in support for model versioning, canary rollouts, autoscaling, and traffic splitting—critical for reliable model iteration and A/B testing.
Advanced Input/Output Handling: Supports request transformation, pre/post-processing, and batch inference pipelines via standard protocol extensions.
Observability and Monitoring: Integrates with Prometheus, OpenTelemetry, and DataHub to provide full visibility into model performance, request latencies, and failure rates.
KServe is used to deploy both real-time and batch models across domains like fraud detection, content ranking, NLP-based document parsing, and user behavior prediction. It integrates closely with other components such as MLflow (for model registry), Kubeflow Pipelines (for orchestration), and Istio (for routing and policy enforcement).
By adopting KServe, you can ensure that its machine learning models are scalable, resilient, and seamlessly integrated into the broader platform—accelerating the delivery of intelligent, model-driven features across the product ecosystem.