Deploying Iris Trained Model on KServe

This guide shows how to train a scikit-learn Iris classifier, serialize it with joblib, upload the artifact to object storage, and deploy it as a scalable inference service on Kubernetes using KServe’s InferenceService CRD.

Prerequisites

A Kubernetes cluster with KServe installed and its CRDs applied.
kubectl configured to talk to your cluster.
Access to object storage reachable from your cluster (S3, MinIO, GCS, etc.).
Python environment with scikit-learn and joblib installed.

Install the Python packages:

pip install scikit-learn joblib

1) Train and Serialize the Iris Model

Train a RandomForest classifier on the Iris dataset and save the model to a file that you can upload to object storage.

from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
import joblib

iris = load_iris()
X = iris.data
y = iris.target

model = RandomForestClassifier()
model.fit(X, y)

# Save the trained model to a file that can be uploaded to object storage
joblib.dump(model, "model.joblib")

Upload model.joblib to your object storage bucket (for example, s3://my-bucket/models/iris/model.joblib) so KServe can fetch it when deploying the service.

A slide titled "Deploy Iris Model on KServe" showing a three-step flow with rounded boxes: "Train & Save Model," "Apply InferenceService," and "Provision Resources," each with an icon and brief description of the step. Arrows connect the steps, describing saving a scikit-learn model to object storage, applying the InferenceService YAML, and creating deployment/pod/service/autoscaler.

2) Create the KServe InferenceService Manifest

KServe uses a Kubernetes CRD called InferenceService to describe model serving resources. The manifest specifies:

the framework (sklearn),
the model.uri pointing to your model artifact in object storage,
optional runtime configuration (resources, predictors, etc).

Example iris.yaml (minimal):

apiVersion: "serving.kserve.io/v1beta1"
kind: InferenceService
metadata:
  name: iris-classifier
spec:
  predictor:
    sklearn:
      storageUri: "s3://my-bucket/models/iris/"

Notes:

For scikit-learn models, KServe expects the model file in the storage URI path. You can provide a folder containing model.joblib or the direct file path depending on the server implementation/version.
Replace the storageUri with your object storage URL and ensure credentials/configuration for that storage are available to the cluster.

3) Apply the InferenceService and Verify

Apply the YAML to create the inference service:

kubectl apply -f iris.yaml

Check that the resource exists and see its status:

kubectl get inferenceservices
kubectl describe inferenceservice iris-classifier
# or output full YAML
kubectl get inferenceservice iris-classifier -o yaml

KServe will create the necessary Kubernetes resources (Deployment/Pods, Service, and autoscaler). Wait until the InferenceService reports readiness before sending requests.

Quick reference: Deploy steps

Step	Purpose	Example / Command
Train & Save	Train a scikit-learn model and serialize it	`joblib.dump(model, "model.joblib")`
Upload	Make model accessible to KServe	`s3://my-bucket/models/iris/model.joblib`
Create InferenceService	Describe serving runtime and model location	`kubectl apply -f iris.yaml`
Verify	Check resource state and readiness	`kubectl get inferenceservices`
Test	Send prediction requests	see curl example below

4) Test the Model Endpoint

When the service is ready, call the REST prediction endpoint. Replace MODEL_URL with your external service URL (for example, http://<hostname>/v1/models/iris-classifier:predict).

curl -X POST "http://MODEL_URL/v1/models/iris-classifier:predict" \
  -H "Content-Type: application/json" \
  -d '{
    "instances": [[5.1, 3.5, 1.4, 0.2]]
  }'

Example of the target URL format (use backticks when documenting placeholders): http://<hostname>/v1/models/iris-classifier:predict. The request sends a single Iris feature vector [sepal length, sepal width, petal length, petal width]. The response will contain predicted classes or probabilities depending on your model runtime configuration.

Ensure the model file is accessible to the KServe runtime. Common approaches are uploading model.joblib to S3/MinIO/GCS and using that remote URL in the model.uri (or storageUri) field of your InferenceService YAML (for example, s3://my-bucket/models/iris/model.joblib). Also confirm any storage credentials are configured in-cluster.

Make sure KServe and its CustomResourceDefinitions (CRDs) are installed and the KServe controller is running in the cluster before applying the InferenceService manifest. Without the controller and CRDs, kubectl apply -f iris.yaml will fail.

Troubleshooting & Debugging Tips

Use kubectl logs for the predictor pod to inspect startup errors:

kubectl get pods -l serving.kserve.io/inferenceservice=iris-classifier
kubectl logs <pod-name>

If the model cannot be downloaded, check storage credentials, bucket policies, and the storageUri value.
To inspect network accessibility, forward or expose the service temporarily:
kubectl port-forward svc/istio-ingressgateway 8080:80 -n istio-system
Then call http://localhost:8080/v1/models/iris-classifier:predict.

Course Introduction

Fundamentals of Kubeflow

Working With Kubeflow

KServe and Katib

Profiles and Multi-Tenancy

Bringing It All Together

Deploying Iris Trained Model on KServe

Prerequisites

1) Train and Serialize the Iris Model

2) Create the KServe InferenceService Manifest

3) Apply the InferenceService and Verify

Quick reference: Deploy steps

4) Test the Model Endpoint

Troubleshooting & Debugging Tips

Links and References

Watch Video

​Prerequisites

​1) Train and Serialize the Iris Model

​2) Create the KServe InferenceService Manifest

​3) Apply the InferenceService and Verify

​Quick reference: Deploy steps

​4) Test the Model Endpoint

​Troubleshooting & Debugging Tips

​Links and References

Watch Video

Prerequisites

1) Train and Serialize the Iris Model

2) Create the KServe InferenceService Manifest

3) Apply the InferenceService and Verify

Quick reference: Deploy steps

4) Test the Model Endpoint

Troubleshooting & Debugging Tips

Links and References