Added code block to describe update_endpoint option in sagemaker model_builder (#4834)

Co-authored-by: Roja Reddy Sareddy <[email protected]>

Author: rsareddy0329

deploy_and_monitor/sm-model_builder/model_builder_handshake.ipynb

@@ -259,6 +259,21 @@
     "print(result)"
    ]
   },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "# Update existing endpoint\n",
+    "predictor = model_builder.deploy(\n",
+    "    endpoint_name=f\"{alias}-xgboost-deploy-realtime\",\n",
+    "    initial_instance_count=3,\n",
+    "    update_endpoint=True,  # Updates existing endpoint\n",
+    ")"
+   ],
+   "id": "74b554744208fa27"
+  },
   {
    "cell_type": "markdown",
    "id": "fbd0e6f6e92d0aeb",
@@ -285,6 +300,24 @@
     "print(result)"
    ]
   },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "predictor = model_builder.deploy(\n",
+    "    endpoint_name=f\"{alias}-xgboost-deploy-serverless\",\n",
+    "    inference_config=ServerlessInferenceConfig(memory_size_in_mb=1024),\n",
+    "    update_endpoint=True,\n",
+    ")\n",
+    "\n",
+    "sklearn_input = np.array([1.0, 2.0, 3.0, 4.0])\n",
+    "result = predictor.predict(sklearn_input)\n",
+    "print(result)"
+   ],
+   "id": "305dca02732c0eac"
+  },
   {
    "cell_type": "markdown",
    "id": "93818038782f105d",
@@ -317,6 +350,28 @@
     "print(result)"
    ]
   },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "predictor = model_builder.deploy(\n",
+    "    endpoint_name=f\"{alias}-xgboost-deploy-async\",\n",
+    "    inference_config=AsyncInferenceConfig(\n",
+    "        output_path=s3_path_join(\n",
+    "            \"s3://\", bucket, f\"{default_bucket_prefix_path}async_inference/update_output_prefix\"\n",
+    "        )\n",
+    "    ),\n",
+    "    update_endpoint=True,\n",
+    ")\n",
+    "\n",
+    "sklearn_input = np.array([1.0, 2.0, 3.0, 4.0])\n",
+    "result = predictor.predict(sklearn_input)\n",
+    "print(result)"
+   ],
+   "id": "bbb56866e2faf7b"
+  },
   {
    "cell_type": "markdown",
    "id": "2ff3e043b5f5f8d7",