diff --git a/google/cloud/aiplatform_v1/__init__.py b/google/cloud/aiplatform_v1/__init__.py
index df58f80445..b60f8a61af 100644
--- a/google/cloud/aiplatform_v1/__init__.py
+++ b/google/cloud/aiplatform_v1/__init__.py
@@ -333,6 +333,7 @@
 from .types.migration_service import SearchMigratableResourcesResponse
 from .types.model import Model
 from .types.model import ModelContainerSpec
+from .types.model import ModelSourceInfo
 from .types.model import Port
 from .types.model import PredictSchemata
 from .types.model_deployment_monitoring_job import (
@@ -857,6 +858,7 @@
     "ModelMonitoringObjectiveConfig",
     "ModelMonitoringStatsAnomalies",
     "ModelServiceClient",
+    "ModelSourceInfo",
     "MutateDeployedIndexOperationMetadata",
     "MutateDeployedIndexRequest",
     "MutateDeployedIndexResponse",
diff --git a/google/cloud/aiplatform_v1/types/__init__.py b/google/cloud/aiplatform_v1/types/__init__.py
index be7bf356b8..b8dd87c849 100644
--- a/google/cloud/aiplatform_v1/types/__init__.py
+++ b/google/cloud/aiplatform_v1/types/__init__.py
@@ -384,6 +384,7 @@
 from .model import (
     Model,
     ModelContainerSpec,
+    ModelSourceInfo,
     Port,
     PredictSchemata,
 )
@@ -885,6 +886,7 @@
     "SearchMigratableResourcesResponse",
     "Model",
     "ModelContainerSpec",
+    "ModelSourceInfo",
     "Port",
     "PredictSchemata",
     "ModelDeploymentMonitoringBigQueryTable",
diff --git a/google/cloud/aiplatform_v1/types/model.py b/google/cloud/aiplatform_v1/types/model.py
index 0dbb2ad510..611a233562 100644
--- a/google/cloud/aiplatform_v1/types/model.py
+++ b/google/cloud/aiplatform_v1/types/model.py
@@ -30,6 +30,7 @@
         "PredictSchemata",
         "ModelContainerSpec",
         "Port",
+        "ModelSourceInfo",
     },
 )
 
@@ -274,6 +275,11 @@ class Model(proto.Message):
             Customer-managed encryption key spec for a
             Model. If set, this Model and all sub-resources
             of this Model will be secured by this key.
+        model_source_info (google.cloud.aiplatform_v1.types.ModelSourceInfo):
+            Output only. Source of a model. It can either
+            be automl training pipeline, custom training
+            pipeline, BigQuery ML, or existing Vertex AI
+            Model.
     """
 
     class DeploymentResourcesType(proto.Enum):
@@ -443,6 +449,11 @@ class ExportableContent(proto.Enum):
         number=24,
         message=gca_encryption_spec.EncryptionSpec,
     )
+    model_source_info = proto.Field(
+        proto.MESSAGE,
+        number=38,
+        message="ModelSourceInfo",
+    )
 
 
 class PredictSchemata(proto.Message):
@@ -800,4 +811,26 @@ class Port(proto.Message):
     )
 
 
+class ModelSourceInfo(proto.Message):
+    r"""Detail description of the source information of the model.
+
+    Attributes:
+        source_type (google.cloud.aiplatform_v1.types.ModelSourceInfo.ModelSourceType):
+            Type of the model source.
+    """
+
+    class ModelSourceType(proto.Enum):
+        r"""Source of the model."""
+        MODEL_SOURCE_TYPE_UNSPECIFIED = 0
+        AUTOML = 1
+        CUSTOM = 2
+        BQML = 3
+
+    source_type = proto.Field(
+        proto.ENUM,
+        number=1,
+        enum=ModelSourceType,
+    )
+
+
 __all__ = tuple(sorted(__protobuf__.manifest))
diff --git a/google/cloud/aiplatform_v1beta1/__init__.py b/google/cloud/aiplatform_v1beta1/__init__.py
index 6b50c2616b..da3382b749 100644
--- a/google/cloud/aiplatform_v1beta1/__init__.py
+++ b/google/cloud/aiplatform_v1beta1/__init__.py
@@ -362,6 +362,7 @@
 from .types.migration_service import SearchMigratableResourcesResponse
 from .types.model import Model
 from .types.model import ModelContainerSpec
+from .types.model import ModelSourceInfo
 from .types.model import Port
 from .types.model import PredictSchemata
 from .types.model_deployment_monitoring_job import (
@@ -905,6 +906,7 @@
     "ModelMonitoringObjectiveConfig",
     "ModelMonitoringStatsAnomalies",
     "ModelServiceClient",
+    "ModelSourceInfo",
     "MutateDeployedIndexOperationMetadata",
     "MutateDeployedIndexRequest",
     "MutateDeployedIndexResponse",
diff --git a/google/cloud/aiplatform_v1beta1/services/migration_service/client.py b/google/cloud/aiplatform_v1beta1/services/migration_service/client.py
index 9ab57a863e..c00bb559e9 100644
--- a/google/cloud/aiplatform_v1beta1/services/migration_service/client.py
+++ b/google/cloud/aiplatform_v1beta1/services/migration_service/client.py
@@ -218,40 +218,40 @@ def parse_dataset_path(path: str) -> Dict[str, str]:
     @staticmethod
     def dataset_path(
         project: str,
-        location: str,
         dataset: str,
     ) -> str:
         """Returns a fully-qualified dataset string."""
-        return "projects/{project}/locations/{location}/datasets/{dataset}".format(
+        return "projects/{project}/datasets/{dataset}".format(
             project=project,
-            location=location,
             dataset=dataset,
         )
 
     @staticmethod
     def parse_dataset_path(path: str) -> Dict[str, str]:
         """Parses a dataset path into its component segments."""
-        m = re.match(
-            r"^projects/(?P<project>.+?)/locations/(?P<location>.+?)/datasets/(?P<dataset>.+?)$",
-            path,
-        )
+        m = re.match(r"^projects/(?P<project>.+?)/datasets/(?P<dataset>.+?)$", path)
         return m.groupdict() if m else {}
 
     @staticmethod
     def dataset_path(
         project: str,
+        location: str,
         dataset: str,
     ) -> str:
         """Returns a fully-qualified dataset string."""
-        return "projects/{project}/datasets/{dataset}".format(
+        return "projects/{project}/locations/{location}/datasets/{dataset}".format(
             project=project,
+            location=location,
             dataset=dataset,
         )
 
     @staticmethod
     def parse_dataset_path(path: str) -> Dict[str, str]:
         """Parses a dataset path into its component segments."""
-        m = re.match(r"^projects/(?P<project>.+?)/datasets/(?P<dataset>.+?)$", path)
+        m = re.match(
+            r"^projects/(?P<project>.+?)/locations/(?P<location>.+?)/datasets/(?P<dataset>.+?)$",
+            path,
+        )
         return m.groupdict() if m else {}
 
     @staticmethod
diff --git a/google/cloud/aiplatform_v1beta1/types/__init__.py b/google/cloud/aiplatform_v1beta1/types/__init__.py
index 0017b2bebc..06d824de56 100644
--- a/google/cloud/aiplatform_v1beta1/types/__init__.py
+++ b/google/cloud/aiplatform_v1beta1/types/__init__.py
@@ -407,6 +407,7 @@
 from .model import (
     Model,
     ModelContainerSpec,
+    ModelSourceInfo,
     Port,
     PredictSchemata,
 )
@@ -931,6 +932,7 @@
     "SearchMigratableResourcesResponse",
     "Model",
     "ModelContainerSpec",
+    "ModelSourceInfo",
     "Port",
     "PredictSchemata",
     "ModelDeploymentMonitoringBigQueryTable",
diff --git a/google/cloud/aiplatform_v1beta1/types/model.py b/google/cloud/aiplatform_v1beta1/types/model.py
index 28ab5cfda4..1e2a735031 100644
--- a/google/cloud/aiplatform_v1beta1/types/model.py
+++ b/google/cloud/aiplatform_v1beta1/types/model.py
@@ -30,6 +30,7 @@
         "PredictSchemata",
         "ModelContainerSpec",
         "Port",
+        "ModelSourceInfo",
     },
 )
 
@@ -274,6 +275,11 @@ class Model(proto.Message):
             Customer-managed encryption key spec for a
             Model. If set, this Model and all sub-resources
             of this Model will be secured by this key.
+        model_source_info (google.cloud.aiplatform_v1beta1.types.ModelSourceInfo):
+            Output only. Source of a model. It can either
+            be automl training pipeline, custom training
+            pipeline, BigQuery ML, or existing Vertex AI
+            Model.
     """
 
     class DeploymentResourcesType(proto.Enum):
@@ -443,6 +449,11 @@ class ExportableContent(proto.Enum):
         number=24,
         message=gca_encryption_spec.EncryptionSpec,
     )
+    model_source_info = proto.Field(
+        proto.MESSAGE,
+        number=38,
+        message="ModelSourceInfo",
+    )
 
 
 class PredictSchemata(proto.Message):
@@ -800,4 +811,26 @@ class Port(proto.Message):
     )
 
 
+class ModelSourceInfo(proto.Message):
+    r"""Detail description of the source information of the model.
+
+    Attributes:
+        source_type (google.cloud.aiplatform_v1beta1.types.ModelSourceInfo.ModelSourceType):
+            Type of the model source.
+    """
+
+    class ModelSourceType(proto.Enum):
+        r"""Source of the model."""
+        MODEL_SOURCE_TYPE_UNSPECIFIED = 0
+        AUTOML = 1
+        CUSTOM = 2
+        BQML = 3
+
+    source_type = proto.Field(
+        proto.ENUM,
+        number=1,
+        enum=ModelSourceType,
+    )
+
+
 __all__ = tuple(sorted(__protobuf__.manifest))
diff --git a/tests/unit/gapic/aiplatform_v1beta1/test_migration_service.py b/tests/unit/gapic/aiplatform_v1beta1/test_migration_service.py
index 8a52fe213b..0185bdabbe 100644
--- a/tests/unit/gapic/aiplatform_v1beta1/test_migration_service.py
+++ b/tests/unit/gapic/aiplatform_v1beta1/test_migration_service.py
@@ -2032,22 +2032,19 @@ def test_parse_dataset_path():
 
 def test_dataset_path():
     project = "squid"
-    location = "clam"
-    dataset = "whelk"
-    expected = "projects/{project}/locations/{location}/datasets/{dataset}".format(
+    dataset = "clam"
+    expected = "projects/{project}/datasets/{dataset}".format(
         project=project,
-        location=location,
         dataset=dataset,
     )
-    actual = MigrationServiceClient.dataset_path(project, location, dataset)
+    actual = MigrationServiceClient.dataset_path(project, dataset)
     assert expected == actual
 
 
 def test_parse_dataset_path():
     expected = {
-        "project": "octopus",
-        "location": "oyster",
-        "dataset": "nudibranch",
+        "project": "whelk",
+        "dataset": "octopus",
     }
     path = MigrationServiceClient.dataset_path(**expected)
 
@@ -2057,19 +2054,22 @@ def test_parse_dataset_path():
 
 
 def test_dataset_path():
-    project = "cuttlefish"
-    dataset = "mussel"
-    expected = "projects/{project}/datasets/{dataset}".format(
+    project = "oyster"
+    location = "nudibranch"
+    dataset = "cuttlefish"
+    expected = "projects/{project}/locations/{location}/datasets/{dataset}".format(
         project=project,
+        location=location,
         dataset=dataset,
     )
-    actual = MigrationServiceClient.dataset_path(project, dataset)
+    actual = MigrationServiceClient.dataset_path(project, location, dataset)
     assert expected == actual
 
 
 def test_parse_dataset_path():
     expected = {
-        "project": "winkle",
+        "project": "mussel",
+        "location": "winkle",
         "dataset": "nautilus",
     }
     path = MigrationServiceClient.dataset_path(**expected)