model-serving-kubernetes
Deploy ML models on Kubernetes with KServe (formerly KFServing) and NVIDIA Triton Inference Server. Includes canary deployments, autoscaling, model versioning, A/B testing, and GPU resource management for production model serving.
npx skills add majiayu000/claude-skill-registry --skill model-serving-kubernetes --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Model Serving on Kubernetes Production ML model serving with KServe and Triton — canary deployments, autoscaling, and GPU-aware scheduling. ## When to Use This Skill Use this skill when: - Serving scikit-learn, PyTorch, TensorFlow, or ONNX models at scale - Implementing canary deployments and A/B testing for ML models - Autoscaling inference pods based on request rate or GPU metrics - Deploying LLMs with Triton or KServe on Kubernetes - Managing multiple model versions with traffic splitting ## Prerequisites - Kubernetes 1.28+ with GPU nodes - KServe installed (or Triton standalone) - `kubectl` and `helm` configured - NVIDIA GPU Operator installed on cluster ## KServe Installation ```bash # Install KServe with Helm helm repo add kserve https://kserve.github.io/helm-charts helm repo update helm install kserve kserve/kserve \ --namespace kserve \ --create-namespace \ --set kserve.controller.gateway.ingressGateway.className=nginx # Verify kubectl get pods -n kserve kubectl get crd | grep kserve ``` ## Basic InferenceService (KServe) ```yaml apiVersion: serving.kserve.io/v1beta1 kind: InferenceService metadata: name: sklearn-iris namespace: models spec: predictor: sklearn: storageUri
- When to Use This Skill
- Prerequisites
- KServe Installation
- Basic InferenceService (KServe)
- GPU-Enabled LLM InferenceService
- Canary Deployment (Traffic Splitting)
- Autoscaling with KEDA
- NVIDIA Triton Inference Server
- Triton Model Repository Structure
- Model Management Commands
- Common Issues
- Best Practices
- Related Skills
Install KServe with Helm helm repo add kserve https://kserve.github.io/helm-charts helm repo update helm install kserve kserve/kserve \ Verify kubectl get pods -n kserve kubectl get crd | grep kserve kubectl apply -f inference-service.yaml Get inference service URL kubectl get inferenceservice sklearn-iris -n models
What does the model-serving-kubernetes skill do?
Deploy ML models on Kubernetes with KServe (formerly KFServing) and NVIDIA Triton Inference Server. Includes canary deployments, autoscaling, model versioning, A/B testing, and GPU resource management for production model serving.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill model-serving-kubernetes --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.