docs: clarify KFP endpoint setup

Author: codex

content/en/docs/components/pipelines/getting-started.md

@@ -44,7 +44,7 @@ You can submit the YAML file to a KFP-conformant backend for execution. If you h
 ```python
 from kfp.client import Client
 
-client = Client(host='<MY-KFP-ENDPOINT>')
+client = Client(host='http://localhost:3000')
 run = client.create_run_from_pipeline_package(
     'pipeline.yaml',
     arguments={
@@ -53,6 +53,12 @@ run = client.create_run_from_pipeline_package(
 )
 ```
 
+Replace `http://localhost:3000` with the endpoint for your Kubeflow Pipelines deployment. For a local standalone deployment, port-forward the `ml-pipeline-ui` service first:
+
+```sh
+kubectl port-forward --namespace kubeflow svc/ml-pipeline-ui 3000:80
+```
+
 The client will print a link to view the pipeline execution graph and logs in the UI. In this case, the pipeline has one task that prints and returns `'Hello, World!'`.
 
 ## Next steps