Batch prediction components

To learn more, run the "Learn how to use prebuilt Pipeline Components to train a custom model" Jupyter notebook in one of the following environments:

Open in Colab | Open in Colab Enterprise | Open in Vertex AI Workbench user-managed notebooks | View on GitHub

The BatchPredictionJob resource lets you run an asynchronous prediction request. Request batch predictions directly from the model resource. You don't need to deploy the model to an endpoint. For data types that support both batch and online predictions you can use batch predictions. This is useful when you don't require an immediate response and want to process accumulated data by using a single request.

To make a batch prediction, specify an input source and an output location for Vertex AI to store predictions results. The inputs and outputs depend on the model type that you're working with. For example, batch predictions for the AutoML image model type require an input JSON Lines file and the name of a Cloud Storage bucket to store the output. For more information about batch prediction, see Get batch predictions.

You can use the ModelBatchPredictOp component to access this resource through Vertex AI Pipelines.

API reference

• For component reference, see the Google Cloud Pipeline Components SDK reference for Batch prediction components.
• For Vertex AI API reference, see the BatchPredictionJob resource page.

Tutorials

• Custom training with prebuilt Google Cloud Pipeline Components

Version history and release notes

To learn more about the version history and changes to the Google Cloud Pipeline Components SDK, see the Google Cloud Pipeline Components SDK Release Notes.

Technical support contacts

If you have any questions, reach out to [e-mail].