feat: add CatBoost model format inference example

docs/model-serving/predictive-inference/frameworks/catboost/catboost-input.json

@@ -0,0 +1,26 @@
+{
+    "inputs": [
+        {
+            "name": "predict-prob",
+            "shape": [
+                1,
+                10
+            ],
+            "datatype": "FP32",
+            "data": [
+                [
+                    61,
+                    56,
+                    27,
+                    59,
+                    15,
+                    50,
+                    60,
+                    77,
+                    62,
+                    98
+                ]
+            ]
+        }
+    ]
+}

docs/model-serving/predictive-inference/frameworks/catboost/catboost.md

@@ -0,0 +1,180 @@
+---
+title: CatBoost
+description: Deploy CatBoost models with KServe
+---
+
+# Deploying CatBoost Models with KServe
+
+This guide demonstrates how to deploy MLflow models using KServe's `InferenceService` and how to send inference requests using the [Open Inference Protocol](https://github.com/kserve/open-inference-protocol).
+
+## Prerequisites
+
+Before you begin, make sure you have:
+
+- A Kubernetes cluster with [KServe installed](../../../../getting-started/quickstart-guide.md).
+- `kubectl` CLI configured to communicate with your cluster.
+- Basic knowledge of Kubernetes concepts and MLflow models.
+- Access to cloud storage (like Google Cloud Storage) to store your model artifacts.
+
+## Training a Sample CatBoost Model
+
+The first step is to train a sample CatBoost model and serialize it in an appropriate format using [save_model](https://catboost.ai/docs/en/concepts/python-reference_catboost_save_model) API.
+
+```python
+import numpy as np
+from catboost import CatBoostClassifier
+
+train_data = np.random.randint(0, 100, size=(100, 10))
+train_labels = np.random.randint(0, 2, size=(100))
+
+model = CatBoostClassifier(
+    iterations=2,
+    depth=2,
+    learning_rate=1,
+    loss_function="Logloss",
+    verbose=True,
+)
+model.fit(train_data, train_labels)
+model.save_model("model.cbm")
+```
+
+## Testing the Model Locally
+
+Once you have your model serialized as `model.cbm`, you can use [MLServer](https://github.com/SeldonIO/MLServer) to create a local model server. For more details, check the [CatBoost example documentation](https://mlserver.readthedocs.io/en/stable/examples/catboost/README.html).
+
+:::tip
+This local testing step is optional. You can skip to the deployment section if you prefer.
+:::
+
+### Prerequisites
+
+To use MLServer locally, install the `mlserver` package and the CatBoost runtime:
+
+```bash
+pip install mlserver mlserver-catboost
+```
+
+### Model Settings
+
+Next, provide model settings so that MLServer knows:
+
+- The inference runtime to serve your model (i.e. `mlserver_catboost.CatboostModel`)
+- The model's parameters
+
+These can be specified through environment variables or by creating a local `model-settings.json` file:
+
+```json
+{
+  "implementation": "mlserver_catboost.CatboostModel",
+  "name": "catboost-classifier",
+  "parameters": {
+    "uri": "./model.cbm",
+    "version": "v0.1.0"
+  }
+}
+```
+
+### Starting the Model Server Locally
+
+With the `mlserver` package installed and a local `model-settings.json` file, start your server with:
+
+```bash
+cat << EOF > model-settings.json
+{
+  "implementation": "mlserver_catboost.CatboostModel",
+  "name": "catboost-classifier",
+  "parameters": {
+    "uri": "./model.cbm",
+    "version": "v0.1.0"
+  }
+}
+EOF
+
+mlserver start .
+```
+
+If everything is fine, you will see such messages in the mlserver process output:
+
+```txt
+2025-09-18 01:15:05,483 [mlserver.parallel] DEBUG - Starting response processing loop...
+2025-09-18 01:15:05,484 [mlserver.rest] INFO - HTTP server running on http://0.0.0.0:8080
+...
+2025-09-18 01:15:06,528 [mlserver][catboost-classifier:v0.1.0] INFO - Loaded model 'catboost-classifier' successfully.
+2025-09-18 01:15:06,529 [mlserver][catboost-classifier:v0.1.0] INFO - Loaded model 'catboost-classifier' successfully.
+```
+
+## Deploying the Model with InferenceService
+
+When deploying the model with InferenceService, KServe injects sensible defaults that work out-of-the-box without additional configuration. However, you can override these defaults by providing a `model-settings.json` file similar to your local one. You can even provide [multiple `model-settings.json` files to load multiple models](https://github.com/SeldonIO/MLServer/tree/master/docs/examples/mms).
+
+To use the Open Inference Protocol (v2) for inference with the deployed model, set the `protocolVersion` field to `v2`. In this example, your model artifacts have already been uploaded to a Google Cloud Storage bucket and can be accessed at `gs://kfserving-examples/models/catboost/classifier`.
+
+Apply the YAML manifest:
+
+```bash
+kubectl apply -f - << EOF
+apiVersion: "serving.kserve.io/v1beta1"
+kind: "InferenceService"
+metadata:
+  name: "catboost-example"
+spec:
+  predictor:
+    model:
+      runtime: kserve-mlserver
+      modelFormat:
+        name: catboost
+      protocolVersion: v2
+      storageUri: "gs://kfserving-examples/models/catboost/classifier"
+      resources:
+        requests:
+          cpu: "1"
+          memory: "1Gi"
+        limits:
+          cpu: "1"
+          memory: "1Gi"
+EOF
+```
+
+## Testing the Deployed Model
+
+You can test your deployed model by sending a sample request following the [Open Inference Protocol](https://github.com/kserve/open-inference-protocol).
+
+Use our sample input file [catboost-input.json](./catboost-input.json) to test the model:
+
+[Determine the ingress IP and ports](../../../../getting-started/predictive-first-isvc.md#4-determine-the-ingress-ip-and-ports), then set the `INGRESS_HOST` and `INGRESS_PORT` environment variables.
+
+```bash
+SERVICE_HOSTNAME=$(kubectl get inferenceservice catboost-example -o jsonpath='{.status.url}' | cut -d "/" -f 3)
+
+curl -v \
+  -H "Host: ${SERVICE_HOSTNAME}" \
+  -H "Content-Type: application/json" \
+  -d @./catboost-input.json \
+  http://${INGRESS_HOST}:${INGRESS_PORT}/v2/models/catboost-example/infer
+```
+
+:::tip[Expected Output]
+```json
+{
+  "model_name": "catboost-example",
+  "id": "70062817-f7de-4105-93ef-e0e2ea3d214e",
+  "parameters": {},
+  "outputs": [
+    {
+      "name": "predict",
+      "shape": [
+        1,
+        1
+      ],
+      "datatype": "INT64",
+      "parameters": {
+        "content_type": "np"
+      },
+      "data": [
+        0
+      ]
+    }
+  ]
+}
+```
+:::

sidebars.ts

@@ -125,6 +125,7 @@ const sidebars: SidebarsConfig = {
                     "model-serving/predictive-inference/frameworks/lightgbm/lightgbm",
                     "model-serving/predictive-inference/frameworks/paddle/paddle",
                     "model-serving/predictive-inference/frameworks/mlflow/mlflow",
+                    "model-serving/predictive-inference/frameworks/catboost/catboost",
                     "model-serving/predictive-inference/frameworks/onnx/onnx",
                     {
                       type: 'category',