Overview of getting predictions on Vertex AI

A prediction is the output of a trained machine learning model. This page provides an overview of the workflow for getting predictions from your models on Vertex AI.

Vertex AI offers two methods for getting prediction:

  • Online predictions are synchronous requests made to a model endpoint. Before sending a request, you must first deploy the model resource to an endpoint. This associates compute resources with the model so that it can serve online predictions with low latency. Use online predictions when you are making requests in response to application input or in situations that require timely inference.

  • Batch predictions are asynchronous requests. You request a batchPredictionsJob directly from the model resource without needing to deploy the model to an endpoint. Use batch predictions when you don't require an immediate response and want to process accumulated data by using a single request.

Get predictions from custom trained models

To get predictions, you must first import your model. After it's imported, it becomes a model resource that is visible in Vertex AI Model Registry.

Then, read the following documentation to learn how to get predictions:

Get predictions from AutoML models

Unlike custom trained models, AutoML models are automatically imported into the Vertex AI Model Registry after training.

Other than that, the workflow for AutoML models is similar, but varies slightly based on your data type and model objective. The documentation for getting AutoML predictions is located alongside the AutoML documentation. Here are links to the documentation:

Image

Learn how to get predictions from the following types of image AutoML models:

Tabular

Learn how to get predictions from the following types of tabular AutoML models:

Text

Learn how to get predictions from the following types of text AutoML models:

Video

Learn how to get predictions from the following types of video AutoML models: