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The Google Cloud (GCPC) SDK provides a set of prebuilt
Kubeflow Pipelines components that are production quality,
performant, and easy to use. You can use Google Cloud Pipeline Components to define and run ML
pipelines in Vertex AI Pipelines and other
ML pipeline execution backends conformant with Kubeflow Pipelines.
For example, you can use these components to complete the following:
Create a new dataset and load different data types into the dataset
(image, tabular, text, or video).
Export data from a dataset to Cloud Storage.
Use AutoML to train a model using image, tabular, or video data.
Run a custom training job using a custom container or a Python package.
Upload an existing model to Vertex AI for batch prediction.
Create a new endpoint and deploy a model to it for online predictions.
Additionally, Google Cloud Pipeline Components supports these prebuilt components
in Vertex AI Pipelines and offers the following benefits:
Easier debugging: Show the underlying resources launched from the
component for simplified debugging.
Standardized artifact types: Provide consistent interfaces to use
standard artifact types for input and
output. Vertex ML Metadata tracks these standard artifacts, making
it easier for you to analyze the lineage of your pipeline's artifacts.
For more details on artifact lineage, see Tracking the lineage of pipeline
artifacts.
Understand pipeline costs with billing labels: Resource labels automatically propagate to Google Cloud services generated by the Google Cloud Pipeline Components in your pipeline run. Use billing labels along with Cloud Billing export to BigQuery to review the cost of your pipeline run. For more information about using labels to understand the cost of a pipeline run, see Understand pipeline run costs. For more information about how labels propagate from a pipeline run to resources spawned by Google Cloud Pipeline Components, see Resource labeling by Vertex AI Pipelines.
Cost efficiencies*: Vertex AI Pipelines optimizes the
execution of these
components by launching the Google Cloud resources, without having to launch the
container.
This reduces the startup latency and reduces the costs of the busy-waiting
container.
*
This feature applies to the following components only:
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-29 UTC."],[],[],null,["# Introduction to Google Cloud Pipeline Components\n\nThe Google Cloud (GCPC) SDK provides a set of prebuilt\nKubeflow Pipelines components that are production quality,\nperformant, and easy to use. You can use Google Cloud Pipeline Components to define and run ML\npipelines in Vertex AI Pipelines and other\nML pipeline execution backends conformant with Kubeflow Pipelines.\n\nFor example, you can use these components to complete the following:\n\n- Create a new dataset and load different data types into the dataset (image, tabular, text, or video).\n- Export data from a dataset to Cloud Storage.\n- Use AutoML to train a model using image, tabular, or video data.\n- Run a custom training job using a custom container or a Python package.\n- Upload an existing model to Vertex AI for batch prediction.\n- Create a new endpoint and deploy a model to it for online predictions.\n\nAdditionally, Google Cloud Pipeline Components supports these prebuilt components\nin Vertex AI Pipelines and offers the following benefits:\n\n- **Easier debugging**: Show the underlying resources launched from the component for simplified debugging.\n- **Standardized artifact types** : Provide consistent interfaces to use [standard artifact types](/vertex-ai/docs/pipelines/artifact-types) for input and output. Vertex ML Metadata tracks these standard artifacts, making it easier for you to analyze the lineage of your pipeline's artifacts. For more details on artifact lineage, see [Tracking the lineage of pipeline\n artifacts](/vertex-ai/docs/pipelines/lineage).\n- **Understand pipeline costs with billing labels** : Resource labels automatically propagate to Google Cloud services generated by the Google Cloud Pipeline Components in your pipeline run. Use billing labels along with Cloud Billing export to BigQuery to review the cost of your pipeline run. For more information about using labels to understand the cost of a pipeline run, see [Understand pipeline run costs](/vertex-ai/docs/pipelines/understand-pipeline-cost-labels). For more information about how labels propagate from a pipeline run to resources spawned by Google Cloud Pipeline Components, see [Resource labeling by Vertex AI Pipelines](/vertex-ai/docs/pipelines/gcpc-label-propagation).\n- **Cost efficiencies** ^\\*^: Vertex AI Pipelines optimizes the execution of these components by launching the Google Cloud resources, without having to launch the container. This reduces the startup latency and reduces the costs of the busy-waiting container.\n\nWhat's next\n-----------\n\n- See all [tutorials that use the Google Cloud SDK](/vertex-ai/docs/pipelines/notebooks).\n- Learn more about specific [Google Cloud Pipeline Components in the reference section](/vertex-ai/docs/pipelines/gcpc-list).\n- Read the official [Google Cloud SDK reference](https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-2.19.0/api/v1/index.html).\n- See the Google Cloud Pipeline Components section in the [Kubeflow Pipelines SDK repository](https://github.com/kubeflow/pipelines/tree/master/components/google-cloud)."]]
