# How to Share Vertex AI Results Without Granting Console Access

Canonical: https://commareports.com/share-vertex-ai-report
Published: 2026-09-14

> Seeing a Vertex AI experiment or pipeline run means an IAM role on the GCP project. Publish the results as a page and keep console access to the people who need it.

# Share Vertex AI results

The evaluation finished. Vertex AI has the metrics, the confusion matrix, the
feature attributions — everything needed to decide whether this model replaces
the one in production.

To look at any of it, a person needs an IAM principal on the GCP project.

## IAM is the wrong lever for this

Granting project access so someone can read a number has costs that outlast the
decision:

- Cloud IAM grants are rarely revoked once made.
- Viewer on a project is considerably more than "can see one evaluation".
- Your security team will ask, correctly, why a product manager has a role on
  the training project.
- And the person still has to navigate the Vertex console to find the run,
  which they will not.

The fallback — screenshotting the metrics panel into Slack — loses every
number's precision and any chance of a follow-up question landing usefully.

## Publish the evaluation

The SDK has what the console shows:

```python
from google.cloud import aiplatform

aiplatform.init(project=PROJECT, location=REGION)
model = aiplatform.Model(model_name=MODEL)
evals = model.list_model_evaluations()
metrics = dict(evals[0].to_dict()["metrics"])
```

Render a page that answers the decision:

1. **Recommendation** — ship, hold, or retrain, in one sentence.
2. **Candidate vs production** — the same metrics, side by side.
3. **Where it gets worse** — the slice that regressed, stated plainly.
4. **Appendix** — dataset version, pipeline run id, model resource name.

Point four is what keeps the console authoritative. Anyone who *does* have IAM
can jump from the claim to the run; everyone else reads the claim.

## Pipelines should publish their own summary

Add a final component to the Kubeflow pipeline that renders and publishes:

```python
@component
def publish_summary(metrics: Input[Metrics], report_id: str):
    ...  # build HTML, PATCH the existing report
```

Now every scheduled retrain updates one URL. The stakeholder's bookmark shows
the current model's evaluation, and the comment history from previous rounds is
still attached — see [publishing from CI](/ci) for the same pattern in a build
pipeline.

## Restricted, not public

Model evaluations frequently reveal what the model was trained on, including
slice names that are themselves sensitive. Publish to a restricted report — see
[security](/security).

## Worth knowing

- **Name the slice that regressed.** A summary that only shows improvement
  gets discovered as incomplete at the worst moment.
- **Comments anchor to the metric**, so "what's the threshold at this recall?"
  lands on the number — see [commenting on HTML](/comment-on-html).
- **5 MB per report body.**

## Try it

Free — unlimited reports, commenters and revisions.

**[Read the API reference →](/docs/api)**

### Related

- [Share an Azure ML result](/share-azure-ml-report) ·
  [Share a SageMaker notebook](/share-sagemaker-notebook)
- [Share an MLflow report](/share-mlflow-report) ·
  [Share a Comet ML experiment](/share-comet-report)
- [Security](/security) · [Comma for data scientists](/for/data-scientists)