diff --git a/config/config.yaml.tftpl b/config/config.yaml.tftpl
index f644a598..65dd2e91 100644
--- a/config/config.yaml.tftpl
+++ b/config/config.yaml.tftpl
@@ -189,6 +189,12 @@ vertex_ai:
           max_concurrent_run_count: 1
           start_time: null
           end_time: null
+          # The `subnetwork` defines the subnetwork in which the pipeline will be executed.
+          # The default value is "default".
+          # Follow the guide: https://cloud.google.com/vertex-ai/docs/general/vpc-peering
+          subnetwork: "default"
+          # If you want to use the vpc network defined above, set the following flag to true
+          use_private_service_access: false
           # The `state` defines the state of the pipeline.
           # In case you don't want to schedule the pipeline, set the state to `PAUSED`.
           state: PAUSED # possible states ACTIVE or PAUSED
@@ -267,6 +273,12 @@ vertex_ai:
           max_concurrent_run_count: 1
           start_time: null
           end_time: null
+          # The `subnetwork` defines the subnetwork in which the pipeline will be executed.
+          # The default value is "default".
+          # Follow the guide: https://cloud.google.com/vertex-ai/docs/general/vpc-peering
+          subnetwork: "default"
+          # If you want to use the vpc network defined above, set the following flag to true
+          use_private_service_access: false
           # The `state` defines the state of the pipeline.
           # In case you don't want to schedule the pipeline, set the state to `PAUSED`.
           state: PAUSED # possible states ACTIVE or PAUSED
@@ -326,6 +338,14 @@ vertex_ai:
           max_concurrent_run_count: 1
           start_time: null
           end_time: null
+          # The `subnetwork` defines the subnetwork in which the pipeline will be executed.
+          # The default value is "default".
+          # Follow the guide: https://cloud.google.com/vertex-ai/docs/general/vpc-peering
+          subnetwork: "default"
+          # If you want to use the vpc network defined above, set the following flag to true
+          use_private_service_access: false
+          # The `state` defines the state of the pipeline.
+          # In case you don't want to schedule the pipeline, set the state to `PAUSED`.
           state: PAUSED # possible states ACTIVE or PAUSED
         pipeline_parameters:
           project_id: "${project_id}"
@@ -381,6 +401,14 @@ vertex_ai:
           max_concurrent_run_count: 1
           start_time: null
           end_time: null
+          # The `subnetwork` defines the subnetwork in which the pipeline will be executed.
+          # The default value is "default".
+          # Follow the guide: https://cloud.google.com/vertex-ai/docs/general/vpc-peering
+          subnetwork: "default"
+          # If you want to use the vpc network defined above, set the following flag to true
+          use_private_service_access: false
+          # The `state` defines the state of the pipeline.
+          # In case you don't want to schedule the pipeline, set the state to `PAUSED`.
           state: PAUSED # possible states ACTIVE or PAUSED
         pipeline_parameters:
           project_id: "${project_id}"
@@ -430,6 +458,14 @@ vertex_ai:
           max_concurrent_run_count: 1
           start_time: null
           end_time: null
+          # The `subnetwork` defines the subnetwork in which the pipeline will be executed.
+          # The default value is "default".
+          # Follow the guide: https://cloud.google.com/vertex-ai/docs/general/vpc-peering
+          subnetwork: "default"
+          # If you want to use the vpc network defined above, set the following flag to true
+          use_private_service_access: false
+          # The `state` defines the state of the pipeline.
+          # In case you don't want to schedule the pipeline, set the state to `PAUSED`.
           state: PAUSED # possible states ACTIVE or PAUSED
         pipeline_parameters:
           project_id: "${project_id}"
@@ -485,6 +521,14 @@ vertex_ai:
           max_concurrent_run_count: 1
           start_time: null
           end_time: null
+          # The `subnetwork` defines the subnetwork in which the pipeline will be executed.
+          # The default value is "default".
+          # Follow the guide: https://cloud.google.com/vertex-ai/docs/general/vpc-peering
+          subnetwork: "default"
+          # If you want to use the vpc network defined above, set the following flag to true
+          use_private_service_access: false
+          # The `state` defines the state of the pipeline.
+          # In case you don't want to schedule the pipeline, set the state to `PAUSED`.
           state: PAUSED # possible states ACTIVE or PAUSED
         pipeline_parameters:
           project_id: "${project_id}"
@@ -525,6 +569,14 @@ vertex_ai:
           max_concurrent_run_count: 1
           start_time: null
           end_time: null
+          # The `subnetwork` defines the subnetwork in which the pipeline will be executed.
+          # The default value is "default".
+          # Follow the guide: https://cloud.google.com/vertex-ai/docs/general/vpc-peering
+          subnetwork: "default"
+          # If you want to use the vpc network defined above, set the following flag to true
+          use_private_service_access: false
+          # The `state` defines the state of the pipeline.
+          # In case you don't want to schedule the pipeline, set the state to `PAUSED`.
           state: PAUSED # possible states ACTIVE or PAUSED
         # These are pipeline parameters that will be passed to the pipeline to be recompiled
         pipeline_parameters:
@@ -600,6 +652,14 @@ vertex_ai:
           max_concurrent_run_count: 1
           start_time: null
           end_time: null
+          # The `subnetwork` defines the subnetwork in which the pipeline will be executed.
+          # The default value is "default".
+          # Follow the guide: https://cloud.google.com/vertex-ai/docs/general/vpc-peering
+          subnetwork: "default"
+          # If you want to use the vpc network defined above, set the following flag to true
+          use_private_service_access: false
+          # The `state` defines the state of the pipeline.
+          # In case you don't want to schedule the pipeline, set the state to `PAUSED`.
           state: PAUSED # possible states ACTIVE or PAUSED
         pipeline_parameters:
           project: "${project_id}"
@@ -639,6 +699,14 @@ vertex_ai:
           max_concurrent_run_count: 1
           start_time: null
           end_time: null
+          # The `subnetwork` defines the subnetwork in which the pipeline will be executed.
+          # The default value is "default".
+          # Follow the guide: https://cloud.google.com/vertex-ai/docs/general/vpc-peering
+          subnetwork: "default"
+          # If you want to use the vpc network defined above, set the following flag to true
+          use_private_service_access: false
+          # The `state` defines the state of the pipeline.
+          # In case you don't want to schedule the pipeline, set the state to `PAUSED`.
           state: PAUSED # possible states ACTIVE or PAUSED
         # These are pipeline parameters that will be passed to the pipeline to be recompiled
         pipeline_parameters:
@@ -734,6 +802,14 @@ vertex_ai:
           max_concurrent_run_count: 1
           start_time: null
           end_time: null
+          # The `subnetwork` defines the subnetwork in which the pipeline will be executed.
+          # The default value is "default".
+          # Follow the guide: https://cloud.google.com/vertex-ai/docs/general/vpc-peering
+          subnetwork: "default"
+          # If you want to use the vpc network defined above, set the following flag to true
+          use_private_service_access: false
+          # The `state` defines the state of the pipeline.
+          # In case you don't want to schedule the pipeline, set the state to `PAUSED`.
           state: PAUSED # possible states ACTIVE or PAUSED
         pipeline_parameters:
           project_id: "${project_id}"
@@ -789,6 +865,14 @@ vertex_ai:
           max_concurrent_run_count: 1
           start_time: null
           end_time: null
+          # The `subnetwork` defines the subnetwork in which the pipeline will be executed.
+          # The default value is "default".
+          # Follow the guide: https://cloud.google.com/vertex-ai/docs/general/vpc-peering
+          subnetwork: "default"
+          # If you want to use the vpc network defined above, set the following flag to true
+          use_private_service_access: false
+          # The `state` defines the state of the pipeline.
+          # In case you don't want to schedule the pipeline, set the state to `PAUSED`.
           state: PAUSED # possible states ACTIVE or PAUSED
         # These are pipeline parameters that will be passed to the pipeline to be recompiled
         pipeline_parameters:
@@ -884,6 +968,14 @@ vertex_ai:
           max_concurrent_run_count: 1
           start_time: null
           end_time: null
+          # The `subnetwork` defines the subnetwork in which the pipeline will be executed.
+          # The default value is "default".
+          # Follow the guide: https://cloud.google.com/vertex-ai/docs/general/vpc-peering
+          subnetwork: "default"
+          # If you want to use the vpc network defined above, set the following flag to true
+          use_private_service_access: false
+          # The `state` defines the state of the pipeline.
+          # In case you don't want to schedule the pipeline, set the state to `PAUSED`.
           state: PAUSED # possible states ACTIVE or PAUSED
         pipeline_parameters:
           project_id: "${project_id}"
@@ -939,6 +1031,14 @@ vertex_ai:
           max_concurrent_run_count: 1
           start_time: null
           end_time: null
+          # The `subnetwork` defines the subnetwork in which the pipeline will be executed.
+          # The default value is "default".
+          # Follow the guide: https://cloud.google.com/vertex-ai/docs/general/vpc-peering
+          subnetwork: "default"
+          # If you want to use the vpc network defined above, set the following flag to true
+          use_private_service_access: false
+          # The `state` defines the state of the pipeline.
+          # In case you don't want to schedule the pipeline, set the state to `PAUSED`.
           state: PAUSED # possible states ACTIVE or PAUSED
         # These are pipeline parameters that will be passed to the pipeline to be compiled
         # For Demographics Audience Segmentation model, we use the BQML KMeans clustering algorithm.
@@ -982,6 +1082,14 @@ vertex_ai:
           max_concurrent_run_count: 1
           start_time: null
           end_time: null
+          # The `subnetwork` defines the subnetwork in which the pipeline will be executed.
+          # The default value is "default".
+          # Follow the guide: https://cloud.google.com/vertex-ai/docs/general/vpc-peering
+          subnetwork: "default"
+          # If you want to use the vpc network defined above, set the following flag to true
+          use_private_service_access: false
+          # The `state` defines the state of the pipeline.
+          # In case you don't want to schedule the pipeline, set the state to `PAUSED`.
           state: PAUSED # possible states ACTIVE or PAUSED
         pipeline_parameters:
           project_id: "${project_id}"
@@ -1028,6 +1136,14 @@ vertex_ai:
           max_concurrent_run_count: 1
           start_time: null
           end_time: null
+          # The `subnetwork` defines the subnetwork in which the pipeline will be executed.
+          # The default value is "default".
+          # Follow the guide: https://cloud.google.com/vertex-ai/docs/general/vpc-peering
+          subnetwork: "default"
+          # If you want to use the vpc network defined above, set the following flag to true
+          use_private_service_access: false
+          # The `state` defines the state of the pipeline.
+          # In case you don't want to schedule the pipeline, set the state to `PAUSED`.
           state: PAUSED # possible states ACTIVE or PAUSED
         # These are pipeline parameters that will be passed to the pipeline to be compiled
         # For Interest based Auto Audience Segmentation model, we use the BQML KMeans clustering algorithm.
@@ -1070,6 +1186,14 @@ vertex_ai:
           max_concurrent_run_count: 1
           start_time: null
           end_time: null
+          # The `subnetwork` defines the subnetwork in which the pipeline will be executed.
+          # The default value is "default".
+          # Follow the guide: https://cloud.google.com/vertex-ai/docs/general/vpc-peering
+          subnetwork: "default"
+          # If you want to use the vpc network defined above, set the following flag to true
+          use_private_service_access: false
+          # The `state` defines the state of the pipeline.
+          # In case you don't want to schedule the pipeline, set the state to `PAUSED`.
           state: PAUSED # possible states ACTIVE or PAUSED
         pipeline_parameters:
           project_id: "${project_id}"
@@ -1119,6 +1243,14 @@ vertex_ai:
           max_concurrent_run_count: 1
           start_time: null
           end_time: null
+          # The `subnetwork` defines the subnetwork in which the pipeline will be executed.
+          # The default value is "default".
+          # Follow the guide: https://cloud.google.com/vertex-ai/docs/general/vpc-peering
+          subnetwork: "default"
+          # If you want to use the vpc network defined above, set the following flag to true
+          use_private_service_access: false
+          # The `state` defines the state of the pipeline.
+          # In case you don't want to schedule the pipeline, set the state to `PAUSED`.
           state: PAUSED # possible states ACTIVE or PAUSED
           # These are pipeline parameters that will be passed to the pipeline to be recompiled
         pipeline_parameters:
@@ -1230,6 +1362,14 @@ vertex_ai:
           max_concurrent_run_count: 1
           start_time: null
           end_time: null
+          # The `subnetwork` defines the subnetwork in which the pipeline will be executed.
+          # The default value is "default".
+          # Follow the guide: https://cloud.google.com/vertex-ai/docs/general/vpc-peering
+          subnetwork: "default"
+          # If you want to use the vpc network defined above, set the following flag to true
+          use_private_service_access: false
+          # The `state` defines the state of the pipeline.
+          # In case you don't want to schedule the pipeline, set the state to `PAUSED`.
           state: PAUSED # possible states ACTIVE or PAUSED
         # These are pipeline parameters that will be passed to the pipeline to be recompiled
         pipeline_parameters:
@@ -1318,6 +1458,14 @@ vertex_ai:
           max_concurrent_run_count: 1
           start_time: null
           end_time: null
+          # The `subnetwork` defines the subnetwork in which the pipeline will be executed.
+          # The default value is "default".
+          # Follow the guide: https://cloud.google.com/vertex-ai/docs/general/vpc-peering
+          subnetwork: "default"
+          # If you want to use the vpc network defined above, set the following flag to true
+          use_private_service_access: false
+          # The `state` defines the state of the pipeline.
+          # In case you don't want to schedule the pipeline, set the state to `PAUSED`.
           state: PAUSED # possible states ACTIVE or PAUSED
         # These are the pipeline parameters to be used in this convoluted prediction pipeline that takes predictions from LTV model and purchase propensity model.
         pipeline_parameters:
@@ -1397,6 +1545,12 @@ vertex_ai:
           max_concurrent_run_count: 1
           start_time: null
           end_time: null
+          # The `subnetwork` defines the subnetwork in which the pipeline will be executed.
+          # The default value is "default".
+          # Follow the guide: https://cloud.google.com/vertex-ai/docs/general/vpc-peering
+          subnetwork: "default"
+          # If you want to use the vpc network defined above, set the following flag to true
+          use_private_service_access: false
           # The `state` defines the state of the pipeline.
           # In case you don't want to schedule the pipeline, set the state to `PAUSED`.
           state: PAUSED # possible states ACTIVE or PAUSED
@@ -1455,6 +1609,12 @@ vertex_ai:
           max_concurrent_run_count: 1
           start_time: null
           end_time: null
+          # The `subnetwork` defines the subnetwork in which the pipeline will be executed.
+          # The default value is "default".
+          # Follow the guide: https://cloud.google.com/vertex-ai/docs/general/vpc-peering
+          subnetwork: "default"
+          # If you want to use the vpc network defined above, set the following flag to true
+          use_private_service_access: false
           # The `state` defines the state of the pipeline.
           # In case you don't want to schedule the pipeline, set the state to `PAUSED`.
           state: PAUSED # possible states ACTIVE or PAUSED
diff --git a/infrastructure/terraform/modules/pipelines/main.tf b/infrastructure/terraform/modules/pipelines/main.tf
index 40faa747..86d363d7 100644
--- a/infrastructure/terraform/modules/pipelines/main.tf
+++ b/infrastructure/terraform/modules/pipelines/main.tf
@@ -52,7 +52,9 @@ module "project_services" {
     "artifactregistry.googleapis.com",
     "aiplatform.googleapis.com",
     "dataflow.googleapis.com",
-    "bigqueryconnection.googleapis.com"
+    "bigqueryconnection.googleapis.com",
+    "servicenetworking.googleapis.com",
+    "compute.googleapis.com"
   ]
 }
 
@@ -160,3 +162,29 @@ resource "null_resource" "check_artifactregistry_api" {
     module.project_services
   ]
 }
+
+# This resource executes gcloud commands to check whether the Service Networking API is enabled.
+# Since enabling APIs can take a few seconds, we need to make the deployment wait until the API is enabled before resuming.
+resource "null_resource" "check_servicenetworking_api" {
+  provisioner "local-exec" {
+    command = <<-EOT
+    COUNTER=0
+    MAX_TRIES=100
+    while ! gcloud services list --project=${module.project_services.project_id} | grep -i "servicenetworking.googleapis.com" && [ $COUNTER -lt $MAX_TRIES ]
+    do
+      sleep 6
+      printf "."
+      COUNTER=$((COUNTER + 1))
+    done
+    if [ $COUNTER -eq $MAX_TRIES ]; then
+      echo "service networking api is not enabled, terraform can not continue!"
+      exit 1
+    fi
+    sleep 20
+    EOT
+  }
+
+  depends_on = [
+    module.project_services
+  ]
+}
diff --git a/infrastructure/terraform/modules/pipelines/pipelines.tf b/infrastructure/terraform/modules/pipelines/pipelines.tf
index 35ee5c34..bd78fa84 100644
--- a/infrastructure/terraform/modules/pipelines/pipelines.tf
+++ b/infrastructure/terraform/modules/pipelines/pipelines.tf
@@ -68,7 +68,8 @@ resource "google_project_iam_member" "pipelines_sa_roles" {
     "roles/artifactregistry.reader",
     "roles/pubsub.publisher",
     "roles/dataflow.developer",
-    "roles/bigquery.connectionUser"
+    "roles/bigquery.connectionUser",
+    "roles/compute.networkUser"
   ])
   role = each.key
 }
diff --git a/pyproject.toml b/pyproject.toml
index 3e0abe24..98a6876a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -23,7 +23,8 @@ packages = [{include = "python"}]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
-google-cloud-aiplatform = "1.52.0"
+#google-cloud-aiplatform = "1.52.0"
+google-cloud-aiplatform = "1.70.0"
 shapely = "<2.0.0"
 google-cloud = "^0.34.0"
 jinja2 = ">=3.0.1,<4.0.0"
@@ -37,11 +38,13 @@ google-cloud-bigquery = "2.30.0"
 google-cloud-pipeline-components = "2.6.0"
 google-auth = "^2.14.1"
 google-cloud-storage = "^2.6.0"
+kfp = "2.4.0"
 ## Fixing this error: https://stackoverflow.com/questions/76175487/sudden-importerror-cannot-import-name-appengine-from-requests-packages-urlli
-kfp = "2.0.0-rc.2"
+#kfp = "2.0.0-rc.2"
 #kfp = {version = "2.0.0-b12", allow-prereleases = true}
 #kfp = {version = "2.0.0-b16", allow-prereleases = true}
-kfp-server-api = "2.0.0-rc.1"
+kfp-server-api = "2.0.5"
+#kfp-server-api = "2.0.0-rc.1"
 #kfp-server-api = "2.0.0.a6"
 #kfp-server-api = "2.0.0b1"
 urllib3 = "1.26.18"
@@ -62,9 +65,11 @@ pyarrow = "15.0.2"
 google-auth-oauthlib = "^1.2.1"
 oauth2client = "^4.1.3"
 google-cloud-core = "^2.4.1"
+sympy="1.13.1"
+google-cloud-resource-manager="1.13.0"
 
 [tool.poetry.group.component_vertex.dependencies]
-google-cloud-aiplatform = "1.52.0"
+google-cloud-aiplatform = "1.70.0"
 shapely = "<2.0.0"
 toml = "0.10.2"
 
diff --git a/python/base_component_image/pyproject.toml b/python/base_component_image/pyproject.toml
index 3ce3fc2f..49aabede 100644
--- a/python/base_component_image/pyproject.toml
+++ b/python/base_component_image/pyproject.toml
@@ -2,25 +2,29 @@
 name = "ma-components"
 version = "1.0.0"
 description = "contains components used in marketing analytics project. the need is to package the components and containerise so that they can be used from the python function based component"
-authors = ["Christos Aniftos <aniftos@google.com>"]
+authors = ["Marketing Analytics Solutions Architects <ma-se@google.com>"]
+license = "Apache 2.0"
 readme = "README.md"
 packages = [{include = "ma_components"}]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
 pip = "23.3"
+kfp = "2.4.0"
 ## Fixing this error: https://stackoverflow.com/questions/76175487/sudden-importerror-cannot-import-name-appengine-from-requests-packages-urlli
-kfp = "2.0.0-rc.2"
+#kfp = "2.0.0-rc.2"
 #kfp = {version = "2.0.0-b12", allow-prereleases = true}
 #kfp = {version = "2.0.0-b16", allow-prereleases = true}
-kfp-server-api = "2.0.0-rc.1"
+kfp-server-api = "2.0.5"
+#kfp-server-api = "2.0.0-rc.1"
 #kfp-server-api = "2.0.0.a6"
 #kfp-server-api = "2.0.0b1"
 urllib3 = "1.26.18"
 toml = "^0.10.2"
 docker = "^6.0.1"
 google-cloud-bigquery = "2.30.0"
-google-cloud-aiplatform = "1.52.0"
+#google-cloud-aiplatform = "1.52.0"
+google-cloud-aiplatform = "1.70.0"
 shapely = "<2.0.0"
 google-cloud-pubsub = "2.15.0"
 #google-cloud-pipeline-components = "1.0.33"
@@ -35,6 +39,8 @@ pyarrow = "15.0.2"
 google-auth-oauthlib = "^1.2.1"
 oauth2client = "^4.1.3"
 google-cloud-core = "^2.4.1"
+sympy="1.13.1"
+google-cloud-resource-manager="1.13.0"
 
 [build-system]
 requires = ["poetry-core>=1.0.0"]
diff --git a/python/pipelines/pipeline_ops.py b/python/pipelines/pipeline_ops.py
index 60a9cd9b..2b152b29 100644
--- a/python/pipelines/pipeline_ops.py
+++ b/python/pipelines/pipeline_ops.py
@@ -17,6 +17,7 @@
 from tracemalloc import start
 
 import pip
+from sympy import preview
 from kfp import compiler
 from google.cloud.aiplatform.pipeline_jobs import PipelineJob, _set_enable_caching_value
 from google.cloud.aiplatform import TabularDataset, Artifact
@@ -625,6 +626,30 @@ def get_gcp_bearer_token() -> str:
     return bearer_token
 
 
+def _get_project_number(project_id) -> str:
+    """
+    Retrieves the project number from a project id
+
+    Returns:
+        A string containing the project number
+    
+    Raises:
+        Exception: If an error occurs while retrieving the resource manager project object.
+    """
+    from google.cloud import resourcemanager_v3
+
+    # Create a resource manager client
+    client = resourcemanager_v3.ProjectsClient()
+
+    # Get the project number
+    project = client.get_project(name=f"projects/{project_id}").name
+    project_number = project.split('/')[-1]
+
+    logging.info(f"Project Number: {project_number}")
+
+    return project_number
+
+
 # Function to schedule the pipeline.
 def schedule_pipeline(
         project_id: str,
@@ -637,6 +662,8 @@ def schedule_pipeline(
         max_concurrent_run_count: str,
         start_time: str,
         end_time: str,
+        subnetwork: str = "default",
+        use_private_service_access: bool = False,
         pipeline_parameters: Dict[str, Any] = None,
         pipeline_parameters_substitutions: Optional[Dict[str, Any]] = None,
     ) -> dict:
@@ -654,6 +681,8 @@ def schedule_pipeline(
         max_concurrent_run_count: The maximum number of concurrent pipeline runs.
         start_time: The start time of the schedule.
         end_time: The end time of the schedule.
+        subnetwork: The VPC subnetwork name to be used in VPC peering.
+        use_private_service_access: A flag to define whether to use the VPC private service access or not.
 
     Returns:
         A dictionary containing information about the scheduled pipeline.
@@ -663,6 +692,9 @@ def schedule_pipeline(
     """
 
     from google.cloud import aiplatform
+    from google.cloud.aiplatform.preview.pipelinejobschedule import (
+        pipeline_job_schedules as preview_pipeline_job_schedules,
+    )
 
     # Substitute pipeline parameters with necessary substitutions
     if pipeline_parameters_substitutions != None:
@@ -680,16 +712,51 @@ def schedule_pipeline(
         display_name=f"{pipeline_name}",
     )
 
-    # Create the schedule with the pipeline job defined
-    pipeline_job_schedule = pipeline_job.create_schedule(
+    # https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform.PipelineJobSchedule
+    # Create a schedule for the pipeline job
+    pipeline_job_schedule = preview_pipeline_job_schedules.PipelineJobSchedule(
         display_name=f"{pipeline_name}",
-        cron=cron,
-        max_concurrent_run_count=max_concurrent_run_count,
-        start_time=start_time,
-        end_time=end_time,
-        service_account=pipeline_sa,
+        pipeline_job=pipeline_job,
+        location=region
     )
 
+    # Get the project number to use in the network identifier
+    project_number = _get_project_number(project_id)
+
+    # Create the schedule using the pipeline job schedule
+    # Using the VPC private service access or not, depending on the flag
+    if use_private_service_access:
+        pipeline_job_schedule.create(
+            cron_expression=cron,
+            max_concurrent_run_count=max_concurrent_run_count,
+            start_time=start_time,
+            end_time=end_time,
+            max_run_count=2,
+            service_account=pipeline_sa,
+            network=f"projects/{project_number}/global/networks/{subnetwork}",
+            create_request_timeout=None,
+        )
+    else:
+        pipeline_job_schedule.create(
+            cron_expression=cron,
+            max_concurrent_run_count=max_concurrent_run_count,
+            start_time=start_time,
+            end_time=end_time,
+            max_run_count=2,
+            service_account=pipeline_sa,
+            create_request_timeout=None,
+        )
+
+    # Old version - Create the schedule with the pipeline job defined
+    #pipeline_job_schedule = pipeline_job.create_schedule(
+    #    display_name=f"{pipeline_name}",
+    #    cron=cron,
+    #    max_concurrent_run_count=max_concurrent_run_count,
+    #    start_time=start_time,
+    #    end_time=end_time,
+    #    service_account=pipeline_sa,
+    #)
+
     logging.info(f"Pipeline scheduled : {pipeline_name}")
 
     return pipeline_job
diff --git a/python/pipelines/scheduler.py b/python/pipelines/scheduler.py
index fbdd9933..ce554ddd 100644
--- a/python/pipelines/scheduler.py
+++ b/python/pipelines/scheduler.py
@@ -138,7 +138,9 @@ def check_extention(file_path: str, type: str = '.yaml'):
                     cron=my_pipeline_vars['schedule']['cron'],
                     max_concurrent_run_count=my_pipeline_vars['schedule']['max_concurrent_run_count'],
                     start_time=my_pipeline_vars['schedule']['start_time'],
-                    end_time=my_pipeline_vars['schedule']['end_time']
+                    end_time=my_pipeline_vars['schedule']['end_time'],
+                    subnetwork=my_pipeline_vars['schedule']['subnetwork'],
+                    use_private_service_access=my_pipeline_vars['schedule']['use_private_service_access'],
         )
 
         if my_pipeline_vars['schedule']['state'] == 'PAUSED':
diff --git a/scripts/common.sh b/scripts/common.sh
index 57d314fa..dbdbff0a 100644
--- a/scripts/common.sh
+++ b/scripts/common.sh
@@ -47,6 +47,7 @@ declare -a apis_array=("cloudresourcemanager.googleapis.com"
                 "bigquerydatatransfer.googleapis.com"
                 "dataform.googleapis.com"
                 "cloudkms.googleapis.com"
+                "servicenetworking.googleapis.com"
                 )
 
 get_project_id() {