diff --git a/metadata-ingestion/examples/ml/mlflow_dh_client_sample.py b/metadata-ingestion/examples/ml/mlflow_dh_client_sample.py
index 2a6a6147d6d1dc..cb7f15345e363c 100644
--- a/metadata-ingestion/examples/ml/mlflow_dh_client_sample.py
+++ b/metadata-ingestion/examples/ml/mlflow_dh_client_sample.py
@@ -1,7 +1,5 @@
 import argparse
-
 from mlflow_dh_client import MLflowDatahubClient
-
 import datahub.metadata.schema_classes as models
 from datahub.metadata.com.linkedin.pegasus2avro.dataprocess import RunResultType
 
@@ -14,11 +12,10 @@
     client = MLflowDatahubClient(token=args.token)
 
     # Create model group
-    # Using property classes directly
     model_group_urn = client.create_model_group(
-        group_id="airline_forecast_models_group_4",
+        group_id="airline_forecast_models_group",
         properties=models.MLModelGroupPropertiesClass(
-            name="Airline Forecast Models Group 4",
+            name="Airline Forecast Models Group",
             description="Group of models for airline passenger forecasting",
             created=models.TimeStampClass(
                 time=1628580000000, actor="urn:li:corpuser:datahub"
@@ -28,14 +25,30 @@
 
     # Creating a model with property classes
     model_urn = client.create_model(
-        model_id="arima_model_5",
+        model_id="arima_model",
         properties=models.MLModelPropertiesClass(
-            name="ARIMA Model 6",
+            name="ARIMA Model",
             description="ARIMA model for airline passenger forecasting",
             customProperties={"team": "forecasting"},
+            trainingMetrics=[
+                models.MLMetricClass(name="accuracy", value="0.9"),
+                models.MLMetricClass(name="precision", value="0.8"),
+            ],
+            hyperParams=[
+                models.MLHyperParamClass(name="learning_rate", value="0.01"),
+                models.MLHyperParamClass(name="batch_size", value="32"),
+            ],
+            externalUrl="https:localhost:5000",
+            created=models.TimeStampClass(
+                time=1628580000000, actor="urn:li:corpuser:datahub"
+            ),
+            lastModified=models.TimeStampClass(
+                time=1628580000000, actor="urn:li:corpuser:datahub"
+            ),
+            tags=["forecasting", "arima"],
         ),
-        version="6.0",
-        alias="arima_model_6_alias",
+        version="1.0",
+        alias="champion",
     )
 
     # Creating an experiment with property class
@@ -45,6 +58,12 @@
             name="Airline Forecast Experiment",
             description="Experiment to forecast airline passenger numbers",
             customProperties={"team": "forecasting"},
+            created=models.TimeStampClass(
+                time=1628580000000, actor="urn:li:corpuser:datahub"
+            ),
+            lastModified=models.TimeStampClass(
+                time=1628580000000, actor="urn:li:corpuser:datahub"
+            ),
         ),
     )
 
@@ -55,11 +74,14 @@
             created=models.AuditStampClass(
                 time=1628580000000, actor="urn:li:corpuser:datahub"
             ),
+            customProperties={"team": "forecasting"},
         ),
         training_run_properties=models.MLTrainingRunPropertiesClass(
             id="simple_training_run_4",
             outputUrls=["s3://my-bucket/output"],
             trainingMetrics=[models.MLMetricClass(name="accuracy", value="0.9")],
+            hyperParams=[models.MLHyperParamClass(name="learning_rate", value="0.01")],
+            externalUrl="https:localhost:5000",
         ),
         run_result=RunResultType.FAILURE,
         start_timestamp=1628580000000,