diff --git a/   ml_ops/sm-mlflow_pipelines/sm-mlflow_pipelines.ipynb b/ml_ops/sm-mlflow_pipelines/sm-mlflow_pipelines.ipynb
similarity index 99%
rename from    ml_ops/sm-mlflow_pipelines/sm-mlflow_pipelines.ipynb
rename to ml_ops/sm-mlflow_pipelines/sm-mlflow_pipelines.ipynb
index 23228e2096..fd2d6ac0f6 100644
--- a/   ml_ops/sm-mlflow_pipelines/sm-mlflow_pipelines.ipynb	
+++ b/ml_ops/sm-mlflow_pipelines/sm-mlflow_pipelines.ipynb
@@ -147,17 +147,17 @@
    "outputs": [],
    "source": [
     "%%writefile requirements.txt\n",
-    "scikit-learn\n",
-    "xgboost==1.7.6\n",
-    "s3fs==0.4.2\n",
-    "sagemaker>=2.199.0,<3\n",
-    "pandas>=2.0.0\n",
+    "scikit-learn==1.6.1\n",
+    "xgboost==2.1.4\n",
+    "s3fs\n",
+    "sagemaker>=2.230.0,<3\n",
+    "pandas>=2.2.3\n",
     "gevent\n",
     "geventhttpclient\n",
     "shap\n",
     "matplotlib\n",
     "fsspec\n",
-    "mlflow==2.13.2\n",
+    "mlflow==2.20.2\n",
     "sagemaker-mlflow==0.1.0"
    ]
   },
@@ -370,12 +370,11 @@
     "            y_train = (train_df.pop(label_column) == \"M\").astype(\"int\")\n",
     "            y_validation = (validation_df.pop(label_column) == \"M\").astype(\"int\")\n",
     "\n",
-    "            xgb = XGBClassifier(n_estimators=num_round, **param)\n",
+    "            xgb = XGBClassifier(n_estimators=num_round, early_stopping_rounds=5,**param)\n",
     "            xgb.fit(\n",
     "                train_df,\n",
     "                y_train,\n",
     "                eval_set=[(validation_df, y_validation)],\n",
-    "                early_stopping_rounds=5,\n",
     "            )\n",
     "\n",
     "        # return xgb\n",