fix: MLFlow E2E Example Notebook (5513) (#5727)

Author: aviruthen

v3-examples/ml-ops-examples/v3-mlflow-train-inference-e2e-example.ipynb

@@ -333,22 +333,24 @@
     "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",
+    "# MLflow 3.x removed RegisteredModel.latest_versions - use search_model_versions instead\n",
+    "latest_version = client.search_model_versions(\n",
+    "    filter_string=f\"name='{MLFLOW_REGISTERED_MODEL_NAME}'\",\n",
+    "    order_by=[\"version_number DESC\"],\n",
+    "    max_results=1\n",
+    ")[0]\n",
+    "\n",
     "model_version = latest_version.version\n",
     "model_source = latest_version.source\n",
     "\n",
-    "# Get S3 URL of model files (for info only)\n",
-    "artifact_uri = client.get_model_version_download_uri(MLFLOW_REGISTERED_MODEL_NAME, model_version)\n",
-    "\n",
     "# MLflow model registry path to use with ModelBuilder\n",
     "mlflow_model_path = f\"models:/{MLFLOW_REGISTERED_MODEL_NAME}/{model_version}\"\n",
     "\n",
     "print(f\"Registered Model: {MLFLOW_REGISTERED_MODEL_NAME}\")\n",
     "print(f\"Latest Version: {model_version}\")\n",
-    "print(f\"Source: {model_source}\")\n",
-    "print(f\"Model artifacts location: {artifact_uri}\")"
+    "print(f\"Source (model artifacts location): {model_source}\")\n",
+    "print(f\"MLflow model path for deployment: {mlflow_model_path}\")"
    ]
   },
   {
@@ -481,19 +483,16 @@
    "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),\n",
+    "    content_type=\"application/json\"\n",
     ")\n",
     "\n",
-    "prediction = json.loads(response['Body'].read().decode('utf-8'))\n",
+    "# Decode and display the result\n",
+    "prediction = json.loads(result.body.read().decode('utf-8'))\n",
     "print(f\"Input: {test_data}\")\n",
     "print(f\"Prediction: {prediction}\")"
    ]