diff --git a/h2o-py-mlflow-flavor/DESCRIPTION.rst b/h2o-py-mlflow-flavor/DESCRIPTION.rst
index cf123bf23506..6d7ade192670 100644
--- a/h2o-py-mlflow-flavor/DESCRIPTION.rst
+++ b/h2o-py-mlflow-flavor/DESCRIPTION.rst
@@ -19,9 +19,9 @@ The model that was trained with H2O-3 runtime can be exported to MLFlow registry
     
     with mlflow.start_run(run_name="myrun") as run:
 	h2o_mlflow_flavor.log_model(h2o_model=h2o_model,
-                                artifact_path="folder",
-                                model_type="MOJO",
-                                extra_prediction_args=["--predictCalibrated"])
+                                    artifact_path="folder",
+                                    model_type="MOJO",
+                                    extra_prediction_args=["--predictCalibrated"])
 
 
 Compared to `log_model` functions of the other flavors being a part of MLFlow, this function has two extra arguments:
@@ -78,10 +78,10 @@ The functions can be utilized as follows:
 	    mlflow.log_metrics(h2o_mlflow_flavor.get_metrics(h2o_model))
 	    input_example = h2o_mlflow_flavor.get_input_example(h2o_model)
 	    h2o_mlflow_flavor.log_model(h2o_model=h2o_model,
-                                    input_example=input_example,
-                                    artifact_path="folder",
-                                    model_type="MOJO",
-                                    extra_prediction_args=["--predictCalibrated"])
+                                        input_example=input_example,
+                                        artifact_path="folder",
+                                        model_type="MOJO",
+                                        extra_prediction_args=["--predictCalibrated"])
 
 
 Model Scoring