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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.
BatchPredictionJob resource page.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 contactsIf you have any questions, reach out to kubeflow-pipelines-components@google.com.
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2025-08-07 UTC.
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