MetadataStores error

#### Environment details

- OS type and version: Windows 11 OS build: 26100.7171
- Python version: 3.11.15
- pip version: pip 24.3.1
- `google-cloud-aiplatform` version: 1.161.0

#### Steps to reproduce

1. Had the same issue with the __name__ attribute when [using PipelineJob.from_pipeline_func()](https://github.com/kubeflow/pipelines/issues/8913) then I switched to the direct complilation method with the compiler.
2. Using uv with python version 3.11.15; Dependencies kfp=2.17.0, google-cloud-pipeline-components=2.22.0, google-cloud-aiplatform=1.161.0
3. Executed script with "uv run python -m main"
4. Made sure execution_service account had the following roles: ai platform admin and service account user.

#### Code example

```python
#main.py
"""Automated Serverless trigger for a Gemini Enterprise Agent Platform Pipeline."""
import kfp
# import functions_framework
from google.cloud import aiplatform
from google_cloud_pipeline_components.v1.custom_job import CustomTrainingJobOp
from kfp import compiler

gc_project_id = "your-gcp-project-id"
gcs_bucket = "your-gcs-bucket-name"
gcs_project_dir = "YOUR-PROJECT-NAME"
pipeline_root_path = f"gs://{gcs_bucket}/{gcs_project_dir}"
workspace_dir = "Workspace-Dir"
staging_dir = "Staging"
gcp_staging_bucket = f"{pipeline_root_path}/{staging_dir}"
gcp_output_dir = f"{pipeline_root_path}/{workspace_dir}"
location = "YOUR-LOCATION"
execution_sa = "your-project-number-compute@developer.gserviceaccount.com"

image_registry = "YOUR-LOCATION-docker.pkg.dev"
image_repository = "your-repository-name"
image_prefix = f"{image_registry}/{gc_project_id}/{image_repository}"

preprocess_image_name = f"{image_prefix}/preprocess:latest"
training_image_name = f"{image_prefix}/training:latest"

@kfp.dsl.pipeline(
name="vertex-ai-pipeline",
description="First Google Cloud Enterprise Agent Pipeline",
pipeline_root=None, # "./"
display_name="First-Pipeline",
pipeline_config=None
)
def vertex_pipeline(
message: str
):
print("Pipeline Message: %s", message)

# from custom_training_job in `components/google-cloud/google_cloud_pipeline_components/v1/custom_job/component.py`
preprocessing_component = CustomTrainingJobOp(
display_name="Preprocessing-Component-Job",
# location=None,
worker_pool_specs=[{
"machine_spec": {"machine_type": "n1-standard-4"},
"replica_count": 1,
"container_spec": {"image_uri": preprocess_image_name}
}],
)
def trigger_ml_pipeline() -> None:
# Authenticates and sets critical settings with Centralized Google Cloud Global configuration object
# `google/cloud/aiplatform/initializer.py` > _Config.init(...)
# made singleton from `google/cloud/aiplatform/__init__.py`
aiplatform.init(
project=gc_project_id,
location=location,
experiment=None,
experiment_description=None,
experiment_tensorboard=None,
staging_bucket=gcp_staging_bucket,
credentials=None,
encryption_spec_key_name=None,
network=None,
service_account=execution_sa, # SET SERVICE ACCOUNT HERE
api_endpoint=None,
api_transport=None,
request_metadata=None
)

compiler.Compiler().compile(
pipeline_func=vertex_pipeline,
package_path="./single-run.json",
pipeline_name="vertex-ai-pipeline",
pipeline_display_name="first-pipeline-run",
pipeline_parameters={
# Adjust to custom pipeline function variables
"message": "Hello-world from console function call!!",
},
type_check=True,
# kubernetes_manifest_format=None,
# kubernetes_manifest_options=None
)

pipeline_job = aiplatform.PipelineJob(
display_name="First Pipeline Job",
template_path="./single-run.json",
job_id=None,
pipeline_root=pipeline_root_path,
parameter_values=None,
input_artifacts=None,
enable_caching=False,
encryption_spec_key_name=None,
labels=None,
credentials=None,
project=None,
location=None,
failure_policy=None,
)

pipeline_job.run(
service_account=execution_sa,
network= None,
reserved_ip_ranges= None,
create_request_timeout= None,
enable_preflight_validations=False,
)
```

#### Stack trace
```
C:\Users\brianperez\Desktop\DEV\Machine-Learning-Projects\Ames-Housing\Google-Cloud-Functions\trigger-platform-pipeline>uv run python -m main
C:\Users\brianperez\Desktop\DEV\Machine-Learning-Projects\Ames-Housing\.venv\Lib\site-packages\google\cloud\aiplatform\models.py:52: FutureWarning: Support for google-cloud-storage < 3.0.0 will be removed in a future version of google-cloud-aiplatform. Please upgrade to google-cloud-storage >= 3.0.0.
from google.cloud.aiplatform.utils import gcs_utils
Pipeline Message: %s {{channel:task=;name=message;type=String;}}
Creating PipelineJob
PipelineJob created. Resource name: projects/613982306205/locations/us-west2/pipelineJobs/vertex-ai-pipeline-20260724122251
To use this PipelineJob in another session:
pipeline_job = aiplatform.PipelineJob.get('projects/613982306205/locations/us-west2/pipelineJobs/vertex-ai-pipeline-20260724122251')
View Pipeline Job:
https://console.cloud.google.com/vertex-ai/locations/us-west2/pipelines/runs/vertex-ai-pipeline-20260724122251?project=613982306205
Traceback (most recent call last):
File "", line 198, in _run_module_as_main
File "", line 88, in _run_code
File "C:\Users\brianperez\Desktop\DEV\Machine-Learning-Projects\Ames-Housing\Google-Cloud-Functions\trigger-platform-pipeline\ye.py", line 157, in
trigger_ml_pipeline()
File "C:\Users\brianperez\Desktop\DEV\Machine-Learning-Projects\Ames-Housing\Google-Cloud-Functions\trigger-platform-pipeline\ye.py", line 140, in trigger_ml_pipeline
pipeline_job.run(
File "C:\Users\brianperez\Desktop\DEV\Machine-Learning-Projects\Ames-Housing\.venv\Lib\site-packages\google\cloud\aiplatform\pipeline_jobs.py", line 334, in run
self._run(
File "C:\Users\brianperez\Desktop\DEV\Machine-Learning-Projects\Ames-Housing\.venv\Lib\site-packages\google\cloud\aiplatform\base.py", line 862, in wrapper
return method(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\brianperez\Desktop\DEV\Machine-Learning-Projects\Ames-Housing\.venv\Lib\site-packages\google\cloud\aiplatform\pipeline_jobs.py", line 382, in _run
self._block_until_complete()
File "C:\Users\brianperez\Desktop\DEV\Machine-Learning-Projects\Ames-Housing\.venv\Lib\site-packages\google\cloud\aiplatform\pipeline_jobs.py", line 799, in _block_until_complete
raise RuntimeError("Job failed with:\n%s" % self._gca_resource.error)
RuntimeError: Job failed with:
code: 7
message: "Failed to create pipeline job. Error: Permission \'aiplatform.metadataStores.get\' denied on resource \'//aiplatform.googleapis.com/projects/613982306205/locations/us-west2/metadataStores/default\' (or it may not exist). Remediate access with this Troubleshooter URL or share it with your administrator - https://console.cloud.google.com/iam-admin/troubleshooter/summary;errorId=CiQwMTlmOTU5NC1hMGFhLTc4YjUtOGRjZS00ZjM1OTYzYmMxZDASP3Byb2plY3RzLzYxMzk4MjMwNjIwNS9sb2NhdGlvbnMvdXMtd2VzdDIvbWV0YWRhdGFTdG9yZXMvZGVmYXVsdA%3D%3D .."

```

According to [Google Cloud Docs](https://docs.cloud.google.com/gemini-enterprise-agent-platform/machine-learning/ml-metadata/configure), the metadata store should create itself on the first run.

I found this stack [from StackOverflow](https://stackoverflow.com/questions/71975726/creating-metadata-store-for-google-cloud-ai-platform) but it did not resolve my issue.

Start at the trigger_ml_pipeline entry point in ye.py and inspect the PipelineJob.run call alongside the reported aiplatform.metadataStores.get denial. Reproduce with the listed Python, kfp, google-cloud-pipeline-components, and google-cloud-aiplatform versions, then compare the configured service account and documented metadata-store setup; done when the pipeline job no longer fails during metadata-store access.

索引モデルが issue の本文から書いたものです。