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KSERVE
Serverless inference platform for generative and predictive AI on Kubernetes
Apache-2.0
ABOUT
Deploying and scaling machine learning models in production across frameworks (PyTorch, TensorFlow, XGBoost, ONNX) and GPU configurations is complex — teams handle infrastructure plumbing instead of modeling. KServe provides a unified Kubernetes-native platform that standardizes inference serving with built-in autoscaling, canary deployments, monitoring, and a serverless architecture that reduces operational costs.
INTEGRATION GUIDE
1. Deploy large language models with vLLM for production-grade inference on Kubernetes
2. Serve predictive ML models (PyTorch, TensorFlow, XGBoost, ONNX) with autoscaling and scale-to-zero
3. Implement canary rollouts and A/B testing for new model version deployments
4. Build multi-step inference pipelines using InferenceGraph for complex serving workflows
5. Achieve cost-efficient GPU serving with request-based autoscaling on Kubernetes clusters
TAGS
kubernetesmodel-servinginferencemlopsllmserverlesscncfvllmkubeflow