Fixing scope for Client Info (#183)

* predicting for only the users with traffic in the past 72h - purchase propensity

* running inference only for users events in the past 72h

* including 72h users for all models predictions

* considering null values in TabWorkflow models

* deleting unused pipfile

* upgrading lib versions

* implementing reporting preprocessing as a new pipeline

* adding more code documentation

* adding important information on the main README.md and DEVELOPMENT.md

* adding schedule run name and more code documentation

* implementing a new scheduler using the vertex ai sdk & adding user_id to procedures for consistency

* adding more code documentation

* adding code doc to the python custom component

* adding more code documentation

* fixing aggregated predictions query

* removing unnecessary resources from deployment

* Writing MDS guide

* adding the MDS developer and troubleshooting documentation

* fixing deployment for activation pipelines and gemini dataset

* Update README.md

* Update README.md

* Update README.md

* Update README.md

* removing deprecated api

* fixing purchase propensity pipelines names

* adding extra condition for when there is not enough data for the window interval to be applied on backfill procedures

* adding more instructions for post deployment and fixing issues when GA4 export was configured for less than 10 days

* removing unnecessary comments

* adding the number of past days to process in the variables files

* adding comment about combining data from different ga4 export datasets to data store

* fixing small issues with feature engineering and ml pipelines

* fixing hyper parameter tuning for kmeans modeling

* fixing optuna parameters

* adding cloud shell image

* fixing the list of all possible users in the propensity training preparation tables

* additional guardrails for when there is not enough data

* adding more documentation

* adding more doc to feature store

* add feature store documentation

* adding ml pipelines docs

* adding ml pipelines docs

* adding more documentation

* adding user agent client info

* fixing scope of client info

---------

Co-authored-by: Carlos Timoteo <[email protected]>

python/pipelines/components/bigquery/component.py

@@ -17,18 +17,6 @@
 import os
 import yaml
 
-from google.api_core.gapic_v1.client_info import ClientInfo
-
-USER_AGENT_FEATURES = 'cloud-solutions/marketing-analytics-jumpstart-features-v1'
-USER_AGENT_PROPENSITY_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-propensity-training-v1'
-USER_AGENT_PROPENSITY_PREDICTION= 'cloud-solutions/marketing-analytics-jumpstart-propensity-prediction-v1'
-USER_AGENT_REGRESSION_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-regression-training-v1'
-USER_AGENT_REGRESSION_PREDICTION = 'cloud-solutions/marketing-analytics-jumpstart-regression-prediction-v1'
-USER_AGENT_SEGMENTATION_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-segmentation-training-v1'
-USER_AGENT_SEGMENTATION_PREDICTION = 'cloud-solutions/marketing-analytics-jumpstart-segmentation-prediction-v1'
-USER_AGENT_VBB_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-vbb-training-v1'
-USER_AGENT_VBB_EXPLANATION = 'cloud-solutions/marketing-analytics-jumpstart-vbb-explanation-v1'
-
 config_file_path = os.path.join(os.path.dirname(
     __file__), '../../../../config/config.yaml')
 
@@ -66,6 +54,18 @@ def bq_stored_procedure_exec(
     from google.cloud import bigquery
     import logging
 
+    from google.api_core.gapic_v1.client_info import ClientInfo
+
+    USER_AGENT_FEATURES = 'cloud-solutions/marketing-analytics-jumpstart-features-v1'
+    USER_AGENT_PROPENSITY_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-propensity-training-v1'
+    USER_AGENT_PROPENSITY_PREDICTION= 'cloud-solutions/marketing-analytics-jumpstart-propensity-prediction-v1'
+    USER_AGENT_REGRESSION_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-regression-training-v1'
+    USER_AGENT_REGRESSION_PREDICTION = 'cloud-solutions/marketing-analytics-jumpstart-regression-prediction-v1'
+    USER_AGENT_SEGMENTATION_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-segmentation-training-v1'
+    USER_AGENT_SEGMENTATION_PREDICTION = 'cloud-solutions/marketing-analytics-jumpstart-segmentation-prediction-v1'
+    USER_AGENT_VBB_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-vbb-training-v1'
+    USER_AGENT_VBB_EXPLANATION = 'cloud-solutions/marketing-analytics-jumpstart-vbb-explanation-v1'
+
     client = bigquery.Client(
         project=project,
         location=location,
@@ -138,6 +138,19 @@ def bq_clustering_exec(
     from google.cloud import bigquery
     import logging
     from datetime import datetime
+
+    from google.api_core.gapic_v1.client_info import ClientInfo
+
+    USER_AGENT_FEATURES = 'cloud-solutions/marketing-analytics-jumpstart-features-v1'
+    USER_AGENT_PROPENSITY_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-propensity-training-v1'
+    USER_AGENT_PROPENSITY_PREDICTION= 'cloud-solutions/marketing-analytics-jumpstart-propensity-prediction-v1'
+    USER_AGENT_REGRESSION_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-regression-training-v1'
+    USER_AGENT_REGRESSION_PREDICTION = 'cloud-solutions/marketing-analytics-jumpstart-regression-prediction-v1'
+    USER_AGENT_SEGMENTATION_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-segmentation-training-v1'
+    USER_AGENT_SEGMENTATION_PREDICTION = 'cloud-solutions/marketing-analytics-jumpstart-segmentation-prediction-v1'
+    USER_AGENT_VBB_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-vbb-training-v1'
+    USER_AGENT_VBB_EXPLANATION = 'cloud-solutions/marketing-analytics-jumpstart-vbb-explanation-v1'
+
 
     model_bq_name = f"{model_name_bq_prefix}_{str(int(datetime.now().timestamp()))}"
 
@@ -238,6 +251,19 @@ def bq_evaluate(
 
     from google.cloud import bigquery
     import json, google.auth, logging
+
+    from google.api_core.gapic_v1.client_info import ClientInfo
+
+    USER_AGENT_FEATURES = 'cloud-solutions/marketing-analytics-jumpstart-features-v1'
+    USER_AGENT_PROPENSITY_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-propensity-training-v1'
+    USER_AGENT_PROPENSITY_PREDICTION= 'cloud-solutions/marketing-analytics-jumpstart-propensity-prediction-v1'
+    USER_AGENT_REGRESSION_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-regression-training-v1'
+    USER_AGENT_REGRESSION_PREDICTION = 'cloud-solutions/marketing-analytics-jumpstart-regression-prediction-v1'
+    USER_AGENT_SEGMENTATION_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-segmentation-training-v1'
+    USER_AGENT_SEGMENTATION_PREDICTION = 'cloud-solutions/marketing-analytics-jumpstart-segmentation-prediction-v1'
+    USER_AGENT_VBB_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-vbb-training-v1'
+    USER_AGENT_VBB_EXPLANATION = 'cloud-solutions/marketing-analytics-jumpstart-vbb-explanation-v1'
+
 
     query = f"""SELECT * FROM ML.EVALUATE(MODEL `{model.metadata["projectId"]}.{model.metadata["datasetId"]}.{model.metadata["modelId"]}`)"""
 
@@ -300,6 +326,19 @@ def bq_select_best_kmeans_model(
     import logging
     from enum import Enum
 
+    from google.api_core.gapic_v1.client_info import ClientInfo
+
+    USER_AGENT_FEATURES = 'cloud-solutions/marketing-analytics-jumpstart-features-v1'
+    USER_AGENT_PROPENSITY_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-propensity-training-v1'
+    USER_AGENT_PROPENSITY_PREDICTION= 'cloud-solutions/marketing-analytics-jumpstart-propensity-prediction-v1'
+    USER_AGENT_REGRESSION_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-regression-training-v1'
+    USER_AGENT_REGRESSION_PREDICTION = 'cloud-solutions/marketing-analytics-jumpstart-regression-prediction-v1'
+    USER_AGENT_SEGMENTATION_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-segmentation-training-v1'
+    USER_AGENT_SEGMENTATION_PREDICTION = 'cloud-solutions/marketing-analytics-jumpstart-segmentation-prediction-v1'
+    USER_AGENT_VBB_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-vbb-training-v1'
+    USER_AGENT_VBB_EXPLANATION = 'cloud-solutions/marketing-analytics-jumpstart-vbb-explanation-v1'
+
+
     class MetricsEnum(Enum):
         DAVIES_BOULDIN_INDEX = 'davies_bouldin_index'
         MEAN_SQUARED_DISCTANCE = 'mean_squared_distance'
@@ -419,6 +458,19 @@ def bq_clustering_predictions(
     from google.cloud import bigquery
     import logging
 
+    from google.api_core.gapic_v1.client_info import ClientInfo
+
+    USER_AGENT_FEATURES = 'cloud-solutions/marketing-analytics-jumpstart-features-v1'
+    USER_AGENT_PROPENSITY_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-propensity-training-v1'
+    USER_AGENT_PROPENSITY_PREDICTION= 'cloud-solutions/marketing-analytics-jumpstart-propensity-prediction-v1'
+    USER_AGENT_REGRESSION_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-regression-training-v1'
+    USER_AGENT_REGRESSION_PREDICTION = 'cloud-solutions/marketing-analytics-jumpstart-regression-prediction-v1'
+    USER_AGENT_SEGMENTATION_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-segmentation-training-v1'
+    USER_AGENT_SEGMENTATION_PREDICTION = 'cloud-solutions/marketing-analytics-jumpstart-segmentation-prediction-v1'
+    USER_AGENT_VBB_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-vbb-training-v1'
+    USER_AGENT_VBB_EXPLANATION = 'cloud-solutions/marketing-analytics-jumpstart-vbb-explanation-v1'
+
+
     timestamp = str(int(datetime.now().timestamp()))
     destination_table.metadata["table_id"] = f"{bigquery_destination_prefix}_{timestamp}"
     model_uri = f"{model.metadata['projectId']}.{model.metadata['datasetId']}.{model.metadata['modelId']}"
@@ -476,6 +528,19 @@ def bq_flatten_tabular_binary_prediction_table(
     from google.cloud import bigquery
     import logging
 
+    from google.api_core.gapic_v1.client_info import ClientInfo
+
+    USER_AGENT_FEATURES = 'cloud-solutions/marketing-analytics-jumpstart-features-v1'
+    USER_AGENT_PROPENSITY_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-propensity-training-v1'
+    USER_AGENT_PROPENSITY_PREDICTION= 'cloud-solutions/marketing-analytics-jumpstart-propensity-prediction-v1'
+    USER_AGENT_REGRESSION_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-regression-training-v1'
+    USER_AGENT_REGRESSION_PREDICTION = 'cloud-solutions/marketing-analytics-jumpstart-regression-prediction-v1'
+    USER_AGENT_SEGMENTATION_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-segmentation-training-v1'
+    USER_AGENT_SEGMENTATION_PREDICTION = 'cloud-solutions/marketing-analytics-jumpstart-segmentation-prediction-v1'
+    USER_AGENT_VBB_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-vbb-training-v1'
+    USER_AGENT_VBB_EXPLANATION = 'cloud-solutions/marketing-analytics-jumpstart-vbb-explanation-v1'
+
+
     # Construct a BigQuery client object.
     client = bigquery.Client(
         project=project_id,
@@ -592,6 +657,19 @@ def bq_flatten_tabular_regression_table(
     from google.cloud import bigquery
     import logging
 
+    from google.api_core.gapic_v1.client_info import ClientInfo
+
+    USER_AGENT_FEATURES = 'cloud-solutions/marketing-analytics-jumpstart-features-v1'
+    USER_AGENT_PROPENSITY_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-propensity-training-v1'
+    USER_AGENT_PROPENSITY_PREDICTION= 'cloud-solutions/marketing-analytics-jumpstart-propensity-prediction-v1'
+    USER_AGENT_REGRESSION_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-regression-training-v1'
+    USER_AGENT_REGRESSION_PREDICTION = 'cloud-solutions/marketing-analytics-jumpstart-regression-prediction-v1'
+    USER_AGENT_SEGMENTATION_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-segmentation-training-v1'
+    USER_AGENT_SEGMENTATION_PREDICTION = 'cloud-solutions/marketing-analytics-jumpstart-segmentation-prediction-v1'
+    USER_AGENT_VBB_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-vbb-training-v1'
+    USER_AGENT_VBB_EXPLANATION = 'cloud-solutions/marketing-analytics-jumpstart-vbb-explanation-v1'
+
+
     # Construct a BigQuery client object.
     client = bigquery.Client(
         project=project_id,
@@ -673,6 +751,19 @@ def bq_flatten_kmeans_prediction_table(
     from google.cloud import bigquery
     import logging
 
+    from google.api_core.gapic_v1.client_info import ClientInfo
+
+    USER_AGENT_FEATURES = 'cloud-solutions/marketing-analytics-jumpstart-features-v1'
+    USER_AGENT_PROPENSITY_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-propensity-training-v1'
+    USER_AGENT_PROPENSITY_PREDICTION= 'cloud-solutions/marketing-analytics-jumpstart-propensity-prediction-v1'
+    USER_AGENT_REGRESSION_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-regression-training-v1'
+    USER_AGENT_REGRESSION_PREDICTION = 'cloud-solutions/marketing-analytics-jumpstart-regression-prediction-v1'
+    USER_AGENT_SEGMENTATION_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-segmentation-training-v1'
+    USER_AGENT_SEGMENTATION_PREDICTION = 'cloud-solutions/marketing-analytics-jumpstart-segmentation-prediction-v1'
+    USER_AGENT_VBB_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-vbb-training-v1'
+    USER_AGENT_VBB_EXPLANATION = 'cloud-solutions/marketing-analytics-jumpstart-vbb-explanation-v1'
+
+
     # Construct a BigQuery client object.
     client = bigquery.Client(
         project=project_id,
@@ -765,6 +856,19 @@ def bq_dynamic_query_exec_output(
     import jinja2
     import re
 
+    from google.api_core.gapic_v1.client_info import ClientInfo
+
+    USER_AGENT_FEATURES = 'cloud-solutions/marketing-analytics-jumpstart-features-v1'
+    USER_AGENT_PROPENSITY_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-propensity-training-v1'
+    USER_AGENT_PROPENSITY_PREDICTION= 'cloud-solutions/marketing-analytics-jumpstart-propensity-prediction-v1'
+    USER_AGENT_REGRESSION_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-regression-training-v1'
+    USER_AGENT_REGRESSION_PREDICTION = 'cloud-solutions/marketing-analytics-jumpstart-regression-prediction-v1'
+    USER_AGENT_SEGMENTATION_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-segmentation-training-v1'
+    USER_AGENT_SEGMENTATION_PREDICTION = 'cloud-solutions/marketing-analytics-jumpstart-segmentation-prediction-v1'
+    USER_AGENT_VBB_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-vbb-training-v1'
+    USER_AGENT_VBB_EXPLANATION = 'cloud-solutions/marketing-analytics-jumpstart-vbb-explanation-v1'
+
+
     # Construct a BigQuery client object.
     client = bigquery.Client(
         project=project_id,
@@ -875,6 +979,19 @@ def bq_dynamic_stored_procedure_exec_output_full_dataset_preparation(
     from google.cloud import bigquery
     import logging
 
+    from google.api_core.gapic_v1.client_info import ClientInfo
+
+    USER_AGENT_FEATURES = 'cloud-solutions/marketing-analytics-jumpstart-features-v1'
+    USER_AGENT_PROPENSITY_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-propensity-training-v1'
+    USER_AGENT_PROPENSITY_PREDICTION= 'cloud-solutions/marketing-analytics-jumpstart-propensity-prediction-v1'
+    USER_AGENT_REGRESSION_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-regression-training-v1'
+    USER_AGENT_REGRESSION_PREDICTION = 'cloud-solutions/marketing-analytics-jumpstart-regression-prediction-v1'
+    USER_AGENT_SEGMENTATION_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-segmentation-training-v1'
+    USER_AGENT_SEGMENTATION_PREDICTION = 'cloud-solutions/marketing-analytics-jumpstart-segmentation-prediction-v1'
+    USER_AGENT_VBB_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-vbb-training-v1'
+    USER_AGENT_VBB_EXPLANATION = 'cloud-solutions/marketing-analytics-jumpstart-vbb-explanation-v1'
+
+
     # Construct a BigQuery client object.
     client = bigquery.Client(
         project=project_id,
@@ -1024,6 +1141,19 @@ def bq_union_predictions_tables(
     from google.cloud import bigquery
     import logging
 
+    from google.api_core.gapic_v1.client_info import ClientInfo
+
+    USER_AGENT_FEATURES = 'cloud-solutions/marketing-analytics-jumpstart-features-v1'
+    USER_AGENT_PROPENSITY_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-propensity-training-v1'
+    USER_AGENT_PROPENSITY_PREDICTION= 'cloud-solutions/marketing-analytics-jumpstart-propensity-prediction-v1'
+    USER_AGENT_REGRESSION_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-regression-training-v1'
+    USER_AGENT_REGRESSION_PREDICTION = 'cloud-solutions/marketing-analytics-jumpstart-regression-prediction-v1'
+    USER_AGENT_SEGMENTATION_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-segmentation-training-v1'
+    USER_AGENT_SEGMENTATION_PREDICTION = 'cloud-solutions/marketing-analytics-jumpstart-segmentation-prediction-v1'
+    USER_AGENT_VBB_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-vbb-training-v1'
+    USER_AGENT_VBB_EXPLANATION = 'cloud-solutions/marketing-analytics-jumpstart-vbb-explanation-v1'
+
+
     # Construct a BigQuery client object.
     client = bigquery.Client(
         project=project_id,
@@ -1191,6 +1321,19 @@ def write_tabular_model_explanation_to_bigquery(
     from google.api_core import exceptions
     import time
 
+    from google.api_core.gapic_v1.client_info import ClientInfo
+
+    USER_AGENT_FEATURES = 'cloud-solutions/marketing-analytics-jumpstart-features-v1'
+    USER_AGENT_PROPENSITY_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-propensity-training-v1'
+    USER_AGENT_PROPENSITY_PREDICTION= 'cloud-solutions/marketing-analytics-jumpstart-propensity-prediction-v1'
+    USER_AGENT_REGRESSION_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-regression-training-v1'
+    USER_AGENT_REGRESSION_PREDICTION = 'cloud-solutions/marketing-analytics-jumpstart-regression-prediction-v1'
+    USER_AGENT_SEGMENTATION_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-segmentation-training-v1'
+    USER_AGENT_SEGMENTATION_PREDICTION = 'cloud-solutions/marketing-analytics-jumpstart-segmentation-prediction-v1'
+    USER_AGENT_VBB_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-vbb-training-v1'
+    USER_AGENT_VBB_EXPLANATION = 'cloud-solutions/marketing-analytics-jumpstart-vbb-explanation-v1'
+
+
     client = bigquery.Client(
         project=project, 
         location=data_location,

python/pipelines/components/python/component.py

@@ -19,18 +19,6 @@
 import os
 import yaml
 
-from google.api_core.gapic_v1.client_info import ClientInfo
-
-USER_AGENT_FEATURES = 'cloud-solutions/marketing-analytics-jumpstart-features-v1'
-USER_AGENT_PROPENSITY_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-propensity-training-v1'
-USER_AGENT_PROPENSITY_PREDICTION= 'cloud-solutions/marketing-analytics-jumpstart-propensity-prediction-v1'
-USER_AGENT_REGRESSION_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-regression-training-v1'
-USER_AGENT_REGRESSION_PREDICTION = 'cloud-solutions/marketing-analytics-jumpstart-regression-prediction-v1'
-USER_AGENT_SEGMENTATION_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-segmentation-training-v1'
-USER_AGENT_SEGMENTATION_PREDICTION = 'cloud-solutions/marketing-analytics-jumpstart-segmentation-prediction-v1'
-USER_AGENT_VBB_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-vbb-training-v1'
-USER_AGENT_VBB_EXPLANATION = 'cloud-solutions/marketing-analytics-jumpstart-vbb-explanation-v1'
-
 config_file_path = os.path.join(os.path.dirname(
     __file__), '../../../../config/config.yaml')
 
@@ -111,6 +99,19 @@ def train_scikit_cluster_model(
     import logging
     from google.cloud import bigquery
 
+    from google.api_core.gapic_v1.client_info import ClientInfo
+
+    USER_AGENT_FEATURES = 'cloud-solutions/marketing-analytics-jumpstart-features-v1'
+    USER_AGENT_PROPENSITY_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-propensity-training-v1'
+    USER_AGENT_PROPENSITY_PREDICTION= 'cloud-solutions/marketing-analytics-jumpstart-propensity-prediction-v1'
+    USER_AGENT_REGRESSION_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-regression-training-v1'
+    USER_AGENT_REGRESSION_PREDICTION = 'cloud-solutions/marketing-analytics-jumpstart-regression-prediction-v1'
+    USER_AGENT_SEGMENTATION_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-segmentation-training-v1'
+    USER_AGENT_SEGMENTATION_PREDICTION = 'cloud-solutions/marketing-analytics-jumpstart-segmentation-prediction-v1'
+    USER_AGENT_VBB_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-vbb-training-v1'
+    USER_AGENT_VBB_EXPLANATION = 'cloud-solutions/marketing-analytics-jumpstart-vbb-explanation-v1'
+
+
     # Construct a BigQuery client object.
     client = bigquery.Client(
         project=project_id,
@@ -323,6 +324,19 @@ def hyper_parameter_tuning_scikit_audience_model(
 
     import logging
     from google.cloud import bigquery
+
+    from google.api_core.gapic_v1.client_info import ClientInfo
+
+    USER_AGENT_FEATURES = 'cloud-solutions/marketing-analytics-jumpstart-features-v1'
+    USER_AGENT_PROPENSITY_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-propensity-training-v1'
+    USER_AGENT_PROPENSITY_PREDICTION= 'cloud-solutions/marketing-analytics-jumpstart-propensity-prediction-v1'
+    USER_AGENT_REGRESSION_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-regression-training-v1'
+    USER_AGENT_REGRESSION_PREDICTION = 'cloud-solutions/marketing-analytics-jumpstart-regression-prediction-v1'
+    USER_AGENT_SEGMENTATION_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-segmentation-training-v1'
+    USER_AGENT_SEGMENTATION_PREDICTION = 'cloud-solutions/marketing-analytics-jumpstart-segmentation-prediction-v1'
+    USER_AGENT_VBB_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-vbb-training-v1'
+    USER_AGENT_VBB_EXPLANATION = 'cloud-solutions/marketing-analytics-jumpstart-vbb-explanation-v1'
+
 
     # Construct a BigQuery client object.
     client = bigquery.Client(