Mantenha tudo organizado com as coleções
Salve e categorize o conteúdo com base nas suas preferências.
O recurso BatchPredictionJob permite executar uma solicitação
de previsão assíncrona. Solicite previsões em lote diretamente do recurso model. Não é necessário implantar o modelo em um endpoint. Para tipos de dados compatíveis com previsões em lote e on-line, use previsões em lote.
Isso é útil quando você não precisa de uma resposta imediata e quer processar os dados acumulados usando uma única solicitação.
Para fazer uma previsão em lote, especifique uma origem de entrada e um local de saída
para a Vertex AI armazenar resultados de previsões. As entradas e saídas dependem do tipo de model com que você está trabalhando. Por exemplo, as previsões
em lote para o tipo de modelo de imagem do AutoML exigem um arquivo de
linhas JSON
de entrada e o nome de um bucket do Cloud Storage para armazenamento da saída.
Para mais informações sobre previsão em lote, consulte
Receber previsões em lote.
Use o componente ModelBatchPredictOp para acessar essa funcionalidade pelo Vertex AI Pipelines.
[[["Fácil de entender","easyToUnderstand","thumb-up"],["Meu problema foi resolvido","solvedMyProblem","thumb-up"],["Outro","otherUp","thumb-up"]],[["Difícil de entender","hardToUnderstand","thumb-down"],["Informações incorretas ou exemplo de código","incorrectInformationOrSampleCode","thumb-down"],["Não contém as informações/amostras de que eu preciso","missingTheInformationSamplesINeed","thumb-down"],["Problema na tradução","translationIssue","thumb-down"],["Outro","otherDown","thumb-down"]],["Última atualização 2025-09-04 UTC."],[],[],null,["# Batch prediction components\n\n| To learn more,\n| run the \"Learn how to use prebuilt Pipeline Components to train a custom model\" notebook in one of the following\n| environments:\n|\n| [Open in Colab](https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/custom_model_training_and_batch_prediction.ipynb)\n|\n|\n| \\|\n|\n| [Open in Colab Enterprise](https://console.cloud.google.com/vertex-ai/colab/import/https%3A%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fpipelines%2Fcustom_model_training_and_batch_prediction.ipynb)\n|\n|\n| \\|\n|\n| [Open\n| in Vertex AI Workbench](https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https%3A%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fpipelines%2Fcustom_model_training_and_batch_prediction.ipynb)\n|\n|\n| \\|\n|\n| [View on GitHub](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/custom_model_training_and_batch_prediction.ipynb)\n\nThe `BatchPredictionJob` resource lets you run an asynchronous\nprediction request. Request batch predictions directly from the `model`\nresource. You don't need to deploy the model to an `endpoint`. For data types\nthat support both batch and online predictions you can use batch predictions.\nThis is useful when you don't require an immediate response and want to process\naccumulated data by using a single request.\n\nTo make a batch prediction, specify an input source and an output location\nfor Vertex AI to store predictions results. The inputs and outputs\ndepend on the `model` type that you're working with. For example, batch\npredictions for the AutoML image model type require an input\n[JSON Lines](https://jsonlines.org/)\nfile and the name of a Cloud Storage bucket to store the output.\nFor more information about batch prediction, see\n[Get batch predictions](/vertex-ai/docs/predictions/batch-predictions).\n\nYou can use the [`ModelBatchPredictOp`](https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-2.19.0/api/v1/batch_predict_job.html#v1.batch_predict_job.ModelBatchPredictOp) component to access this resource through Vertex AI Pipelines.\n\nAPI reference\n-------------\n\n- For component reference, see the [Google Cloud SDK reference for Batch prediction components](https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-2.19.0/api/v1/batch_predict_job.html).\n- For Vertex AI API reference, see the [`BatchPredictionJob` resource](/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs) page.\n\nTutorials\n---------\n\n- [Custom training with prebuilt Google Cloud Pipeline Components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/custom_model_training_and_batch_prediction.ipynb)\n\n### Version history and release notes\n\nTo learn more about the version history and changes to the Google Cloud Pipeline Components SDK, see the [Google Cloud Pipeline Components SDK Release Notes](https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-2.19.0/release.html).\n\n### Technical support contacts\n\nIf you have any questions, reach out to\n[kubeflow-pipelines-components@google.com](mailto: kubeflow-pipelines-components@google.com)."]]
