diff --git a/label_studio_ml/server.py b/label_studio_ml/server.py
index b71866899..c53c87386 100644
--- a/label_studio_ml/server.py
+++ b/label_studio_ml/server.py
@@ -160,7 +160,7 @@ def deploy_to_gcp(args):
     auth_token = subprocess.check_output(' '.join(["gcloud", "auth", "print-identity-token"]), shell=True)
     if not auth_token:
         raise PermissionError("You are not authentificated in gcloud! Please run gcloud auth login.")
-    # configurate project
+    # configure project
     subprocess.check_output(' '.join(["gcloud", "config", "set", "project", project_id]), shell=True)
     # deploy service
     subprocess.check_output(' '.join([
@@ -170,6 +170,8 @@ def deploy_to_gcp(args):
         "--region", region,
         "--update-env-vars", f"LABEL_STUDIO_ML_BACKEND_V2=1,LABEL_STUDIO_HOSTNAME={args.label_studio_host},LABEL_STUDIO_API_KEY={args.label_studio_api_key}"
     ]), input=b"y", shell=True)
+    print(Fore.GREEN + 'Congratulations! ML Backend service has been successfully initialized in GCP.')
+    print(Fore.GREEN + 'Get service URL, go on Project Settings -> Machine Learning and add ML backend.')
 
 
 def special_match(strg, search=re.compile(r'[^a-z-]').search):
diff --git a/label_studio_ml/utils.py b/label_studio_ml/utils.py
index be5ae7426..31cbb1466 100644
--- a/label_studio_ml/utils.py
+++ b/label_studio_ml/utils.py
@@ -1,7 +1,18 @@
+import os
 import logging
+import tarfile
+import datetime
+import google.auth
 
 from PIL import Image
 
+from google.api_core.exceptions import NotFound
+from google.cloud import artifactregistry_v1beta2
+from google.cloud.artifactregistry_v1beta2 import CreateRepositoryRequest, Repository
+from google.cloud.devtools import cloudbuild_v1
+from google.cloud import storage as google_storage
+from google.cloud.devtools.cloudbuild_v1 import Source, StorageSource
+
 from label_studio_tools.core.utils.params import get_env
 from label_studio_tools.core.utils.io import get_local_path
 
@@ -48,3 +59,68 @@ def get_image_local_path(url, image_cache_dir=None, project_dir=None, image_dir=
 
 def get_image_size(filepath):
     return Image.open(filepath).size
+
+
+def deploy_to_gcp(args):
+    # Setup env before hand: https://cloud.google.com/run/docs/setup
+    # Prepare dirs with code and docker file
+    # Set configuration params
+    region = args.gcp_region or os.environ.get("GCP_REGION", "us-central1")
+    project_id = args.gcp_project or os.environ.get("GCP_PROJECT")
+    service_name = args.project_name
+    output_dir = os.path.join(args.root_dir, args.project_name)
+    time_stamp = str(datetime.now().timestamp())
+    # create tgz file to upload
+    output_filename = os.path.join(output_dir, f"{time_stamp}.tgz")
+    with tarfile.open(output_filename, "w:gz") as tar:
+        tar.add(output_dir, arcname=".")
+    # get current credentials and project
+    credentials, project = google.auth.load_credentials_from_file(r"C:\projects\Heartex\TestData\gcs\i-portfolio-339416-807c5a11ea6f.json")
+    artifact_registry_name = 'cloud-run-source-deploy'
+    # Upload artifacts to GCP
+    # Get registry
+    registry_client = artifactregistry_v1beta2.ArtifactRegistryClient(credentials=credentials)
+    try:
+        repo_name = f"projects/{project_id}/locations/{region}/repositories/{artifact_registry_name}"
+        repo = registry_client.get_repository(name=repo_name)
+    except NotFound:
+        if not repo:
+            create_repository_request = CreateRepositoryRequest()
+            create_repository_request.repository = Repository()
+            create_repository_request.repository.name = repo_name
+            create_repository_request.repository.description = 'Cloud Run Source Deployments'
+            create_repository_request.repository.format_ = Repository.Format.DOCKER
+            repo = registry_client.create_repository(create_repository_request)
+    except Exception as e:
+        logger.error("Error while creating Artifact Repository.", exc_info=True)
+        logger.error(e)
+
+    # Get storage link
+    storage_client = google_storage.Client(project=project_id, credentials=credentials)
+    bucket_name = f"{project_id}_cloudbuild"
+    bucket = storage_client.lookup_bucket(bucket_name)
+    if not bucket:
+        bucket = storage_client.create_bucket(bucket_name, project=project_id)
+
+    # Upload files
+    with open(output_filename, mode='rb') as file:
+        blob = bucket.blob(f"{time_stamp}.tgz")
+        blob.upload_from_file(file)
+
+    # Post build
+    build_client = cloudbuild_v1.CloudBuildClient(credentials=credentials)
+    build = cloudbuild_v1.Build()
+    build.images = [f"us-central1-docker.pkg.dev/{project_id}/{artifact_registry_name}/{service_name}"]
+    build.source = Source()
+    build.source.storage_source = StorageSource()
+    build.source.storage_source.bucket = bucket_name
+    build.source.storage_source.generation = blob.generation
+    build.source.storage_source.object_ = f"{time_stamp}.tgz"
+    build.steps = [{"args": ["build", "--network", "cloudbuild", "--no-cache", "-t",
+                            f"us-central1-docker.pkg.dev/{project_id}/{artifact_registry_name}/{service_name}",
+                            "."], "name": "gcr.io/cloud-builders/docker"}]
+
+    build_operation = build_client.create_build(project_id=project_id, build=build)
+    build_result = build_operation.result()
+    artifact = build_result.artifacts.images[0]
+    return artifact