diff --git a/v3-examples/ml-ops-examples/v3-mlflow-train-inference-e2e-example.ipynb b/v3-examples/ml-ops-examples/v3-mlflow-train-inference-e2e-example.ipynb
index 80435c9325..e8457f4561 100644
--- a/v3-examples/ml-ops-examples/v3-mlflow-train-inference-e2e-example.ipynb
+++ b/v3-examples/ml-ops-examples/v3-mlflow-train-inference-e2e-example.ipynb
@@ -35,7 +35,8 @@
    "outputs": [],
    "source": [
     "# Install fix for MLflow path resolution issues\n",
-    "%pip install mlflow==3.4.0"
+    "# Using minimum version constraint so the notebook stays compatible with future releases\n",
+    "%pip install 'mlflow>=3.4.0'"
    ]
   },
   {
@@ -60,6 +61,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import json\n",
     "import uuid\n",
     "from sagemaker.core import image_uris\n",
     "from sagemaker.core.helper.session_helper import Session\n",
@@ -71,7 +73,8 @@
     "MLFLOW_TRACKING_ARN = \"XXXXX\"\n",
     "\n",
     "# AWS Configuration\n",
-    "AWS_REGION = Session.boto_region_name\n",
+    "sagemaker_session = Session()\n",
+    "AWS_REGION = sagemaker_session.boto_region_name\n",
     "\n",
     "# Get PyTorch training image dynamically\n",
     "PYTORCH_TRAINING_IMAGE = image_uris.retrieve(\n",
@@ -304,6 +307,7 @@
     "        requirements=\"requirements.txt\",\n",
     "    ),\n",
     "    base_job_name=training_job_name,\n",
+    "    sagemaker_session=sagemaker_session,\n",
     ")\n",
     "\n",
     "# Start training job\n",
@@ -333,9 +337,16 @@
     "from mlflow import MlflowClient\n",
     "\n",
     "client = MlflowClient()\n",
-    "registered_model = client.get_registered_model(name=MLFLOW_REGISTERED_MODEL_NAME)\n",
     "\n",
-    "latest_version = registered_model.latest_versions[0]\n",
+    "# Use search_model_versions (compatible with MLflow 3.x)\n",
+    "# Note: order_by field name may vary across MLflow versions;\n",
+    "# 'creation_timestamp' is broadly supported.\n",
+    "model_versions = client.search_model_versions(\n",
+    "    filter_string=f\"name='{MLFLOW_REGISTERED_MODEL_NAME}'\",\n",
+    "    order_by=['creation_timestamp DESC'],\n",
+    "    max_results=1\n",
+    ")\n",
+    "latest_version = model_versions[0]\n",
     "model_version = latest_version.version\n",
     "model_source = latest_version.source\n",
     "\n",
@@ -366,54 +377,23 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import json\n",
-    "import torch\n",
-    "from sagemaker.serve.marshalling.custom_payload_translator import CustomPayloadTranslator\n",
     "from sagemaker.serve.builder.schema_builder import SchemaBuilder\n",
     "\n",
     "# =============================================================================\n",
-    "# Custom translators for PyTorch tensor conversion\n",
-    "# \n",
-    "# PyTorch models expect tensors, but SageMaker endpoints communicate via JSON.\n",
-    "# These translators handle the conversion between JSON payloads and PyTorch tensors.\n",
+    "# Schema Builder for MLflow Model\n",
+    "#\n",
+    "# When deploying from MLflow Model Registry, the MLflow pyfunc wrapper handles\n",
+    "# serialization/deserialization automatically. We only need to provide sample\n",
+    "# input/output for schema inference - no custom translators needed.\n",
     "# =============================================================================\n",
     "\n",
-    "class PyTorchInputTranslator(CustomPayloadTranslator):\n",
-    "    \"\"\"Handles input serialization/deserialization for PyTorch models.\"\"\"\n",
-    "    def __init__(self):\n",
-    "        super().__init__(content_type='application/json', accept_type='application/json')\n",
-    "    \n",
-    "    def serialize_payload_to_bytes(self, payload: object) -> bytes:\n",
-    "        if isinstance(payload, torch.Tensor):\n",
-    "            return json.dumps(payload.tolist()).encode('utf-8')\n",
-    "        return json.dumps(payload).encode('utf-8')\n",
-    "    \n",
-    "    def deserialize_payload_from_stream(self, stream) -> object:\n",
-    "        data = json.load(stream)\n",
-    "        return torch.tensor(data, dtype=torch.float32)\n",
-    "\n",
-    "class PyTorchOutputTranslator(CustomPayloadTranslator):\n",
-    "    \"\"\"Handles output serialization/deserialization for PyTorch models.\"\"\"\n",
-    "    def __init__(self):\n",
-    "        super().__init__(content_type='application/json', accept_type='application/json')\n",
-    "    \n",
-    "    def serialize_payload_to_bytes(self, payload: object) -> bytes:\n",
-    "        if isinstance(payload, torch.Tensor):\n",
-    "            return json.dumps(payload.tolist()).encode('utf-8')\n",
-    "        return json.dumps(payload).encode('utf-8')\n",
-    "    \n",
-    "    def deserialize_payload_from_stream(self, stream) -> object:\n",
-    "        return json.load(stream)\n",
-    "\n",
     "# Sample input/output for schema inference\n",
     "sample_input = [[0.1, 0.2, 0.3, 0.4]]\n",
     "sample_output = [[0.8, 0.2]]\n",
     "\n",
     "schema_builder = SchemaBuilder(\n",
     "    sample_input=sample_input,\n",
-    "    sample_output=sample_output,\n",
-    "    input_translator=PyTorchInputTranslator(),\n",
-    "    output_translator=PyTorchOutputTranslator()\n",
+    "    sample_output=sample_output\n",
     ")"
    ]
   },
@@ -434,7 +414,7 @@
     "        \"MLFLOW_MODEL_PATH\": mlflow_model_path,\n",
     "        \"MLFLOW_TRACKING_ARN\": MLFLOW_TRACKING_ARN\n",
     "    },\n",
-    "    dependencies={\"auto\": False, \"custom\": [\"mlflow==3.4.0\", \"sagemaker==3.3.1\", \"numpy==2.4.1\", \"cloudpickle==3.1.2\"]},\n",
+    "    dependencies={\"auto\": False, \"custom\": [\"mlflow>=3.4.0\", \"sagemaker>=3.3.1\", \"numpy>=2.4.1\", \"cloudpickle>=3.1.2\"]},\n",
     ")\n",
     "\n",
     "print(f\"ModelBuilder configured with MLflow model: {mlflow_model_path}\")"
@@ -481,19 +461,22 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import boto3\n",
-    "\n",
     "# Test with JSON input\n",
     "test_data = [[0.1, 0.2, 0.3, 0.4]]\n",
     "\n",
-    "runtime_client = boto3.client('sagemaker-runtime')\n",
-    "response = runtime_client.invoke_endpoint(\n",
-    "    EndpointName=core_endpoint.endpoint_name,\n",
-    "    Body=json.dumps(test_data),\n",
-    "    ContentType='application/json'\n",
+    "result = core_endpoint.invoke(\n",
+    "    body=json.dumps(test_data).encode('utf-8'),\n",
+    "    content_type='application/json'\n",
     ")\n",
     "\n",
-    "prediction = json.loads(response['Body'].read().decode('utf-8'))\n",
+    "# The invoke() response body may be a streaming object or bytes;\n",
+    "# handle both cases for robustness.\n",
+    "response_body = result.body\n",
+    "if hasattr(response_body, 'read'):\n",
+    "    response_body = response_body.read()\n",
+    "if isinstance(response_body, bytes):\n",
+    "    response_body = response_body.decode('utf-8')\n",
+    "prediction = json.loads(response_body)\n",
     "print(f\"Input: {test_data}\")\n",
     "print(f\"Prediction: {prediction}\")"
    ]
@@ -551,7 +534,9 @@
     "- `ModelBuilder` with `MLFLOW_MODEL_PATH` - deploy from registry\n",
     "\n",
     "Key patterns:\n",
-    "- Custom `PayloadTranslator` classes for PyTorch tensor serialization\n"
+    "- MLflow pyfunc handles model serialization automatically\n",
+    "- `SchemaBuilder` with sample input/output for schema inference\n",
+    "- `core_endpoint.invoke()` for V3-style endpoint invocation\n"
    ]
   }
  ],