KServe — Scalable ML Model Serving on Kubernetes

What it is

KServe is a CNCF project that provides a standardized, Kubernetes-native platform for deploying, scaling, and managing machine learning models in production. It supports inference runtimes including TensorFlow, PyTorch, XGBoost, vLLM, and custom containers.

ML engineers, platform teams, and MLOps practitioners use KServe to deploy models behind a consistent API without writing custom serving infrastructure. It handles autoscaling, canary rollouts, and model versioning through Kubernetes custom resources.

How it saves time or tokens

KServe abstracts away the complexity of serving infrastructure. Instead of writing custom Flask or FastAPI servers for each model, you declare an InferenceService resource and KServe handles routing, scaling (including scale-to-zero), and load balancing. This reduces deployment time from days to minutes and eliminates boilerplate serving code.

How to use

1. Install KServe on your Kubernetes cluster using the provided manifests or Helm chart.
2. Create an InferenceService YAML defining your model location and runtime.
3. Apply the resource and KServe provisions the serving pods, configures autoscaling, and exposes a prediction endpoint.

Example

apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
  name: sklearn-iris
spec:
  predictor:
    model:
      modelFormat:
        name: sklearn
      storageUri: 'gs://kserve-examples/models/sklearn/1.0/model'
      resources:
        requests:
          cpu: '1'
          memory: 2Gi

# Deploy the model
kubectl apply -f sklearn-iris.yaml

# Test the prediction endpoint
curl -X POST http://sklearn-iris.default.example.com/v1/models/sklearn-iris:predict \
  -H 'Content-Type: application/json' \
  -d '{"instances": [[5.1, 3.5, 1.4, 0.2]]}'

Related on TokRepo

• AI Tools for DevOps — Kubernetes and infrastructure automation tools
• AI Tools for Automation — ML pipeline and deployment automation

Common pitfalls

• Not configuring resource requests and limits, leading to OOM kills on large models.
• Enabling scale-to-zero without understanding cold start latency for your use case.
• Using the default Knative setup without tuning concurrency and queue depth for your traffic patterns.