diff --git a/ingestion/examples/sample_data/models/models.json b/ingestion/examples/sample_data/models/models.json
index e7d4e1c0b25..06c42db8f38 100644
--- a/ingestion/examples/sample_data/models/models.json
+++ b/ingestion/examples/sample_data/models/models.json
@@ -4,7 +4,55 @@
       "displayName": "ETA Predictions",
       "description": "ETA Predictions Model",
       "algorithm": "Neural Network",
-      "dashboard": "sample_superset.eta_predictions_performance"
+      "dashboard": "sample_superset.eta_predictions_performance",
+      "mlStore": {
+        "storage": "s3://path-to-pickle",
+        "imageRepository": "https://docker.hub.com/image"
+      },
+      "server": "http://my-server.ai",
+      "mlFeatures": [
+        {
+          "name": "sales",
+          "dataType": "numerical",
+          "description": "Sales amount",
+          "featureSources": [
+            {
+              "name": "gross_sales",
+              "dataType": "integer",
+              "dataSource": "sample_data.ecommerce_db.shopify.fact_sale"
+            }
+          ]
+        },
+        {
+          "name": "persona",
+          "dataType": "categorical",
+          "description": "type of buyer",
+          "featureAlgorithm": "PCA",
+          "featureSources": [
+            {
+              "name": "membership",
+              "dataType": "string",
+              "dataSource": "sample_data.ecommerce_db.shopify.raw_customer"
+            },
+            {
+              "name": "platform",
+              "dataType": "string",
+              "dataSource": "sample_data.ecommerce_db.shopify.raw_customer"
+            }
+          ]
+        }
+      ],
+      "mlHyperParameters": [
+        {
+          "name": "regularisation",
+          "value": "0.5"
+        },
+        {
+          "name": "random",
+          "value": "hello"
+        }
+      ],
+      "target": "ETA_time"
     },
       {
       "name": "forecast_sales",
diff --git a/ingestion/src/metadata/ingestion/source/sample_data.py b/ingestion/src/metadata/ingestion/source/sample_data.py
index d9238cc3e2f..472b2373d15 100644
--- a/ingestion/src/metadata/ingestion/source/sample_data.py
+++ b/ingestion/src/metadata/ingestion/source/sample_data.py
@@ -32,6 +32,12 @@ from metadata.generated.schema.api.tests.createTableTest import CreateTableTestR
 from metadata.generated.schema.entity.data.database import Database
 from metadata.generated.schema.entity.data.databaseSchema import DatabaseSchema
 from metadata.generated.schema.entity.data.location import Location, LocationType
+from metadata.generated.schema.entity.data.mlmodel import (
+    FeatureSource,
+    MlFeature,
+    MlHyperParameter,
+    MlStore,
+)
 from metadata.generated.schema.entity.data.pipeline import Pipeline, PipelineStatus
 from metadata.generated.schema.entity.data.table import Table
 from metadata.generated.schema.entity.data.topic import Topic
@@ -545,6 +551,36 @@ class SampleDataSource(Source[Entity]):
                     pipeline_status=PipelineStatus(**status),
                 )
 
+    def get_ml_feature_sources(self, feature: dict) -> List[FeatureSource]:
+        """
+        Build FeatureSources from sample data
+        """
+        return [
+            FeatureSource(
+                name=source["name"],
+                dataType=source["dataType"],
+                dataSource=self.metadata.get_entity_reference(
+                    entity=Table, fqdn=source["dataSource"]
+                ),
+            )
+            for source in feature.get("featureSources", [])
+        ]
+
+    def get_ml_features(self, model: dict) -> List[MlFeature]:
+        """
+        Build MlFeatures from sample data
+        """
+        return [
+            MlFeature(
+                name=feature["name"],
+                dataType=feature["dataType"],
+                description=feature.get("description"),
+                featureAlgorithm=feature.get("featureAlgorithm"),
+                featureSources=self.get_ml_feature_sources(feature),
+            )
+            for feature in model.get("mlFeatures", [])
+        ]
+
     def ingest_mlmodels(self) -> Iterable[CreateMlModelRequest]:
         """
         Convert sample model data into a Model Entity
@@ -571,6 +607,19 @@ class SampleDataSource(Source[Entity]):
                     description=model["description"],
                     algorithm=model["algorithm"],
                     dashboard=EntityReference(id=dashboard_id, type="dashboard"),
+                    mlStore=MlStore(
+                        storage=model["mlStore"]["storage"],
+                        imageRepository=model["mlStore"]["imageRepository"],
+                    )
+                    if model.get("mlStore")
+                    else None,
+                    server=model.get("server"),
+                    target=model.get("target"),
+                    mlFeatures=self.get_ml_features(model),
+                    mlHyperParameters=[
+                        MlHyperParameter(name=param["name"], value=param["value"])
+                        for param in model.get("mlHyperParameters", [])
+                    ],
                 )
                 yield model_ev
             except Exception as err: