Register BigQuery ML models to Vertex AI

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Overview

When you create a BigQuery ML model you can use the optional model_registry training option in CREATE MODEL syntax to register the model to Vertex AI Model Registry. The CREATE MODEL syntax also contains options for adding the model ID and the version alias, which can be used for streamlined deployment and model management.

Once your BigQuery ML model has finished training from the BigQuery ML side, it automatically displays in the Vertex AI Model Registry alongside your other models. From the Source column, you can see where your models are sourced from. A quick way to find your BigQuery ML models is to filter by source.

Once your BigQuery ML model is registered, you can use Vertex AI Model Registry functionalities with your model. You can deploy to an endpoint, compare model versions, make predictions, monitor your models, and view model evaluations from the Evaluations tab.

Remember, BigQuery ML models are not automatically registered to the Vertex AI Model Registry. All models created using BigQuery ML still display in the BigQuery ML user interface, regardless of Vertex AI Model Registry registration.

Add a Vertex AI model ID

To help model management, you can specify a Vertex AI model ID which is associated with your BigQuery ML model. This ID is visible from the Vertex AI Model Registry, and if you decide to upload another version of your BigQuery ML model to the Vertex AI Model Registry with the same ID, it's automatically added as a new model version.

The Vertex AI model ID does not accept uppercase letters. If the Vertex AI model ID is not specified, the BigQuery ML model ID is used. In this case, make sure the BigQuery ML model ID is also lowercase. To see a full list of the model ID requirements, see the specifications in upload reference documentation.

Add a Vertex AI model alias

Model aliases are helpful for fetching or deploying a particular model version by reference without needing to know the specific version's ID. In this way, they operate similarly to Docker Tags or Branch references in Git.

To learn more about how Vertex AI Model Registry aliases work, see How to use model version aliases.

Register a BigQuery ML model to the Vertex AI Model Registry

To register a new BigQuery ML model with Vertex AI Model Registry, you must run the CREATE MODEL syntax. To learn more, see The CREATE MODEL statement from the reference documentation. When you create a new model using the CREATE MODEL syntax, the model_registry="vertex_ai" line in the SQL command is required to register your BigQuery ML model.

To register an existing BigQuery ML model with Vertex AI Model Registry, see Register existing trained models.

CREATE MODEL syntax

{CREATE MODEL| CREATE MODEL IF NOT EXISTS| CREATE OR REPLACE MODEL}
model_name
[TRANSFORM (select_list)]
[OPTIONS
(MODEL_REGISTRY = {'VERTEX_AI' }
   [,VERTEX_AI_MODEL_ID = string_value ]
   [,VERTEX_AI_MODEL_VERSION_ALIASES = string_array ]
   , ...)
   

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