diff --git a/mlops/unit_2_training/custom/dashboard_data_source.py b/mlops/unit_2_training/custom/dashboard_data_source.py
index 79cdb3dda..d7cead9f7 100644
--- a/mlops/unit_2_training/custom/dashboard_data_source.py
+++ b/mlops/unit_2_training/custom/dashboard_data_source.py
@@ -10,12 +10,12 @@
 
 @custom
 def source(
+    model: Booster,
     settings: Tuple[
         Dict[str, Union[bool, float, int, str]],
         csr_matrix,
         Series,
     ],
-    model: Booster, 
     **kwargs,
 ) -> Tuple[Booster, csr_matrix, csr_matrix]:
     X_train, y_train, _ = settings
diff --git a/mlops/unit_2_training/data_exporters/xgboost.py b/mlops/unit_2_training/data_exporters/xgboost.py
index 456729f18..9ecc9501a 100644
--- a/mlops/unit_2_training/data_exporters/xgboost.py
+++ b/mlops/unit_2_training/data_exporters/xgboost.py
@@ -12,6 +12,7 @@
 
 @data_exporter
 def train(
+    training_set: Dict[str, Union[Series, csr_matrix]],
     settings: Tuple[
         Dict[str, Union[bool, float, int, str]],
         csr_matrix,
@@ -31,5 +32,6 @@ def train(
         hyperparameters,
         verbose_eval=kwargs.get('verbose_eval', 100),
     )
-
-    return model
+    
+    # DictVectorizer for online inference.
+    return model, training_set['build'][6]
diff --git a/mlops/unit_4_triggering/pipelines/xgboost_training/metadata.yaml b/mlops/unit_4_triggering/pipelines/xgboost_training/metadata.yaml
index 90ddf8fe9..eac434ca5 100755
--- a/mlops/unit_4_triggering/pipelines/xgboost_training/metadata.yaml
+++ b/mlops/unit_4_triggering/pipelines/xgboost_training/metadata.yaml
@@ -52,14 +52,14 @@ blocks:
   language: python
   name: XGBoost
   retry_config: null
-  status: executed
+  status: updated
   timeout: null
   type: data_exporter
   upstream_blocks:
   - training_set
   - hyperparameter_tuning/xgboost
   uuid: xgboost
-- all_upstream_blocks_executed: true
+- all_upstream_blocks_executed: false
   color: pink
   configuration:
     file_path: custom/dashboard_data_source.py
@@ -72,7 +72,7 @@ blocks:
   language: python
   name: Dashboard data source
   retry_config: null
-  status: failed
+  status: executed
   timeout: null
   type: custom
   upstream_blocks:
@@ -106,7 +106,5 @@ variables:
   early_stopping_rounds: 1
   max_depth: 1
   max_evaluations: 1
-  verbose_eval: 10000
-  verbosity: 0
-variables_dir: /home/src/mage_data/unit_4_triggering
+variables_dir: /root/.mage_data/unit_4_triggering
 widgets: []
diff --git a/mlops/utils/logging.py b/mlops/utils/logging.py
index adb2ca735..532f35d01 100644
--- a/mlops/utils/logging.py
+++ b/mlops/utils/logging.py
@@ -98,47 +98,48 @@ def track_experiment(
 
     dataset_inputs = []
 
-    for dataset_name, dataset, tags in [
-        ('dataset', training_set, dict(context='training')),
-        (
-            'targets',
-            training_targets.to_numpy() if training_targets is not None else None,
-            dict(context='training'),
-        ),
-        ('dataset', validation_set, dict(context='validation')),
-        (
-            'targets',
-            validation_targets.to_numpy() if validation_targets is not None else None,
-            dict(context='validation'),
-        ),
-        ('predictions', predictions, dict(context='training')),
-    ]:
-        if dataset is None:
-            continue
-
-        dataset_from = None
-        if isinstance(dataset, pd.DataFrame):
-            dataset_from = from_pandas
-        elif isinstance(dataset, np.ndarray):
-            dataset_from = from_numpy
-
-        if dataset_from:
-            ds = dataset_from(dataset, name=dataset_name)._to_mlflow_entity()
-            ds_input = DatasetInput(ds, tags=[InputTag(k, v) for k, v in tags.items()])
-            dataset_inputs.append(ds_input)
-
-        if verbosity:
-            context = tags['context']
-            if dataset_from:
-                print(f'Logged input for {context} {dataset_name}.')
-            else:
-                print(
-                    f'Unable to log input for {context} {dataset_name}, '
-                    f'{type(dataset)} not registered.'
-                )
-
-    if len(dataset_inputs) >= 1:
-        client.log_inputs(run_id, dataset_inputs)
+    # This increases memory too much.
+    # for dataset_name, dataset, tags in [
+    #     ('dataset', training_set, dict(context='training')),
+    #     (
+    #         'targets',
+    #         training_targets.to_numpy() if training_targets is not None else None,
+    #         dict(context='training'),
+    #     ),
+    #     ('dataset', validation_set, dict(context='validation')),
+    #     (
+    #         'targets',
+    #         validation_targets.to_numpy() if validation_targets is not None else None,
+    #         dict(context='validation'),
+    #     ),
+    #     ('predictions', predictions, dict(context='training')),
+    # ]:
+    #     if dataset is None:
+    #         continue
+
+    #     dataset_from = None
+    #     if isinstance(dataset, pd.DataFrame):
+    #         dataset_from = from_pandas
+    #     elif isinstance(dataset, np.ndarray):
+    #         dataset_from = from_numpy
+
+    #     if dataset_from:
+    #         ds = dataset_from(dataset, name=dataset_name)._to_mlflow_entity()
+    #         ds_input = DatasetInput(ds, tags=[InputTag(k, v) for k, v in tags.items()])
+    #         dataset_inputs.append(ds_input)
+
+    #     if verbosity:
+    #         context = tags['context']
+    #         if dataset_from:
+    #             print(f'Logged input for {context} {dataset_name}.')
+    #         else:
+    #             print(
+    #                 f'Unable to log input for {context} {dataset_name}, '
+    #                 f'{type(dataset)} not registered.'
+    #             )
+
+    # if len(dataset_inputs) >= 1:
+    #     client.log_inputs(run_id, dataset_inputs)
 
     if model:
         log_model = None