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Tune a model using your data | BigQuery

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Tune a model using your data

This document shows you how to create a BigQuery ML remote model that references a Vertex AI model, and then configure the model to perform supervised tuning. The Vertex AI model must be one of the following:

After you create the remote model, you use the ML.EVALUATE function to evaluate the model and confirm that the model's performance suits your use case. You can then use the model in conjunction with the ML.GENERATE_TEXT function to analyze text in a BigQuery table.

For more information, see Vertex AI Gemini API model supervised tuning.

Required roles

To create and evaluate a tuned model, you need the following Identity and Access Management (IAM) roles:

These predefined roles contain the permissions required to perform the tasks in this document. To see the exact permissions that are required, expand the Required permissions section:

Required permissions

You might also be able to get these permissions with custom roles or other predefined roles.

Before you begin

  1. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

    Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

    Go to project selector

  2. Verify that billing is enabled for your Google Cloud project.

  3. Enable the BigQuery, BigQuery Connection,Vertex AI, and Compute Engine APIs.

    Enable the APIs

Create a dataset

Create a BigQuery dataset to contain your resources:

Console

  1. In the Google Cloud console, go to the BigQuery page.

    Go to the BigQuery page

  2. In the Explorer pane, click your project name.

  3. Click more_vert View actions > Create dataset.

  4. On the Create dataset page, do the following:

bq

  1. To create a new dataset, use the bq mk command with the --location flag:

    bq --location=LOCATION mk -d DATASET_ID

    Replace the following:

  2. Confirm that the dataset was created:

    bq ls
Create a connection

You can skip this step if you either have a default connection configured, or you have the BigQuery Admin role.

Create a Cloud resource connection for the remote model to use, and get the connection's service account. Create the connection in the same location as the dataset that you created in the previous step.

Select one of the following options:

Console

  1. Go to the BigQuery page.

    Go to BigQuery

  2. In the Explorer pane, click add Add data:

    The Add data dialog opens.

  3. In the Filter By pane, in the Data Source Type section, select Business Applications.

    Alternatively, in the Search for data sources field, you can enter Vertex AI.

  4. In the Featured data sources section, click Vertex AI.

  5. Click the Vertex AI Models: BigQuery Federation solution card.

  6. In the Connection type list, select Vertex AI remote models, remote functions and BigLake (Cloud Resource).

  7. In the Connection ID field, enter a name for your connection.

  8. Click Create connection.

  9. Click Go to connection.

  10. In the Connection info pane, copy the service account ID for use in a later step.

bq

  1. In a command-line environment, create a connection:

    bq mk --connection --location=REGION --project_id=PROJECT_ID \
    --connection_type=CLOUD_RESOURCE CONNECTION_ID

    The --project_id parameter overrides the default project.

    Replace the following:

    When you create a connection resource, BigQuery creates a unique system service account and associates it with the connection.

    Troubleshooting: If you get the following connection error, update the Google Cloud SDK:

    Flags parsing error: flag --connection_type=CLOUD_RESOURCE: value should be one of...

  2. Retrieve and copy the service account ID for use in a later step:

    bq show --connection PROJECT_ID.REGION.CONNECTION_ID

    The output is similar to the following:

    name                          properties
1234.REGION.CONNECTION_ID     {"serviceAccountId": "connection-1234-9u56h9@gcp-sa-bigquery-condel.iam.gserviceaccount.com"}
Terraform

Use the google_bigquery_connection resource.

Note: To create BigQuery objects using Terraform, you must enable the Cloud Resource Manager API.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for client libraries.

The following example creates a Cloud resource connection named my_cloud_resource_connection in the US region:

To apply your Terraform configuration in a Google Cloud project, complete the steps in the following sections.

Prepare Cloud Shell
  1. Launch Cloud Shell.

  2. Set the default Google Cloud project where you want to apply your Terraform configurations.

    You only need to run this command once per project, and you can run it in any directory.

    export GOOGLE_CLOUD_PROJECT=PROJECT_ID

    Environment variables are overridden if you set explicit values in the Terraform configuration file.

Prepare the directory

Each Terraform configuration file must have its own directory (also called a root module).

  1. In Cloud Shell, create a directory and a new file within that directory. The filename must have the .tf extension—for example main.tf. In this tutorial, the file is referred to as main.tf. 
    mkdir DIRECTORY && cd DIRECTORY && touch main.tf

  2. If you are following a tutorial, you can copy the sample code in each section or step.

    Copy the sample code into the newly created main.tf.

    Optionally, copy the code from GitHub. This is recommended when the Terraform snippet is part of an end-to-end solution.

  3. Review and modify the sample parameters to apply to your environment.
  4. Save your changes.
  5. Initialize Terraform. You only need to do this once per directory. 
    terraform init

    Optionally, to use the latest Google provider version, include the -upgrade option:

    terraform init -upgrade
Apply the changes
  1. Review the configuration and verify that the resources that Terraform is going to create or update match your expectations: 
    terraform plan

    Make corrections to the configuration as necessary.

  2. Apply the Terraform configuration by running the following command and entering yes at the prompt: 
    terraform apply

    Wait until Terraform displays the "Apply complete!" message.

  3. Open your Google Cloud project to view the results. In the Google Cloud console, navigate to your resources in the UI to make sure that Terraform has created or updated them.
Note: Terraform samples typically assume that the required APIs are enabled in your Google Cloud project. Give the connection's service account access

Grant the connection's service account the Vertex AI Service Agent role.

If you plan to specify the endpoint as a URL when you create the remote model, for example endpoint = 'https://us-central1-aiplatform.googleapis.com/v1/projects/myproject/locations/us-central1/publishers/google/models/gemini-2.0-flash', grant this role in the same project you specify in the URL.

If you plan to specify the endpoint by using the model name when you create the remote model, for example endpoint = 'gemini-2.0-flash', grant this role in the same project where you plan to create the remote model.

Granting the role in a different project results in the error bqcx-1234567890-wxyz@gcp-sa-bigquery-condel.iam.gserviceaccount.com does not have the permission to access resource.

To grant the role, follow these steps:

Console

  1. Go to the IAM & Admin page.

    Go to IAM & Admin

  2. Click person_add Grant Access.

  3. For New principals, enter the service account ID that you copied earlier.

  4. Click Select a role.

  5. In Filter, type Vertex AI Service Agent and then select that role.

  6. Click Save.

gcloud

Use the gcloud projects add-iam-policy-binding command:

gcloud projects add-iam-policy-binding 'PROJECT_NUMBER' --member='serviceAccount:MEMBER' --role='roles/aiplatform.serviceAgent' --condition=None

Replace the following:

The service account associated with your connection is an instance of the BigQuery Connection Delegation Service Agent, so it is OK to assign a service agent role to it.

Create a model with supervised tuning

  1. In the Google Cloud console, go to the BigQuery page.

    Go to BigQuery

  2. In the query editor, run the following query to create a remote model:

    CREATE OR REPLACE MODEL
`PROJECT_ID.DATASET_ID.MODEL_NAME`
REMOTE WITH CONNECTION {DEFAULT | `PROJECT_ID.REGION.CONNECTION_ID`}
OPTIONS (
  ENDPOINT = 'ENDPOINT',
  MAX_ITERATIONS = MAX_ITERATIONS,
  LEARNING_RATE_MULTIPLIER = LEARNING_RATE_MULTIPLIER,
  DATA_SPLIT_METHOD = 'DATA_SPLIT_METHOD',
  DATA_SPLIT_EVAL_FRACTION = DATA_SPLIT_EVAL_FRACTION,
  DATA_SPLIT_COL = 'DATA_SPLIT_COL',
  EVALUATION_TASK = 'EVALUATION_TASK',
  PROMPT_COL = 'INPUT_PROMPT_COL',
  INPUT_LABEL_COLS = INPUT_LABEL_COLS)
AS SELECT PROMPT_COLUMN, LABEL_COLUMN
FROM `TABLE_PROJECT_ID.TABLE_DATASET.TABLE_NAME`;

    Replace the following:

Evaluate the tuned model

  1. In the Google Cloud console, go to the BigQuery page.

    Go to BigQuery

  2. In the query editor, run the following query to evaluate the tuned model:

    SELECT
*
FROM
ML.EVALUATE(
  MODEL `PROJECT_ID.DATASET_ID.MODEL_NAME`,
  TABLE `TABLE_PROJECT_ID.TABLE_DATASET.TABLE_NAME`,
  STRUCT('TASK_TYPE' AS task_type, TOKENS AS max_output_tokens,
    TEMPERATURE AS temperature, TOP_K AS top_k,
    TOP_P AS top_p));

    Replace the following:

Generate text

Generate text with the ML.GENERATE_TEXT function:

Prompt column

Generate text by using a table column to provide the prompt.

SELECT *
FROM ML.GENERATE_TEXT(
  MODEL `PROJECT_ID.DATASET_ID.MODEL_NAME`,
  TABLE PROJECT_ID.DATASET_ID.TABLE_NAME,
  STRUCT(TOKENS AS max_output_tokens, TEMPERATURE AS temperature,
  TOP_P AS top_p, FLATTEN_JSON AS flatten_json_output,
  STOP_SEQUENCES AS stop_sequences)
);

Replace the following:

Prompt query

Generate text by using a query to provide the prompt.

SELECT *
FROM ML.GENERATE_TEXT(
  MODEL `PROJECT_ID.DATASET_ID.MODEL_NAME`,
  (PROMPT_QUERY),
  STRUCT(TOKENS AS max_output_tokens, TEMPERATURE AS temperature,
  TOP_P AS top_p, FLATTEN_JSON AS flatten_json_output,
  STOP_SEQUENCES AS stop_sequences)
);

Replace the following:

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.

[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-07 UTC."],[],[]]

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