Overview of creating managed datasets on Vertex AI

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You can use a managed dataset to provide the source data used to train AutoML and custom models on Vertex AI. A managed dataset is required for AutoML and is optional for custom training.

Create a managed dataset for AutoML models

You can create managed datasets for training AutoML models by using the Google Cloud console or the Vertex AI API. The instructions for how to do this slightly vary based on your data type and model objective. Start by preparing your training data.

Image

Learn how to create a managed dataset for the following types of image AutoML models:

• Image classification models
• Image object detection models

Tabular

Learn how to create a managed dataset for the following types of tabular AutoML models:

• Tabular classification and regression models
• Tabular forecasting models

Text

Learn how to create a managed dataset for the following types of text AutoML models:

• Text classification models
• Text entity extraction models
• Text sentiment analysis models

Video

Learn how to create a managed dataset for the following types of video AutoML models:

• Video action recognition models
• Video classification models
• Video object tracking models

Create a managed dataset for custom trained models

The instructions on how to create a managed dataset for training custom models are the same, regardless of your data type or model objective.

For details, see Use managed datasets.

View managed datasets using Data Catalog

Data Catalog is a fully managed, scalable metadata management service that provides a centralized location to search for datasets across projects and regions.

For details, see Use Data Catalog to search for model and dataset resources overview.