Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from http://cloud.google.com/bigquery/docs/remote-function-tutorial below:

Annotate images with the ML.ANNOTATE_IMAGE function | BigQuery

Stay organized with collections Save and categorize content based on your preferences.

This document describes how to use the ML.ANNOTATE_IMAGE function with a remote model to annotate images from an object table.

To create a remote model and annotate images, you need the following Identity and Access Management (IAM) roles at the project level:

• Create and use BigQuery datasets, tables, and models: BigQuery Data Editor (roles/bigquery.dataEditor)

• Create, delegate, and use BigQuery connections: BigQuery Connections Admin (roles/bigquery.connectionsAdmin)

  If you don't have a default connection configured, you can create and set one as part of running the CREATE MODEL statement. To do so, you must have BigQuery Admin (roles/bigquery.admin) on your project. For more information, see Configure the default connection.

• Grant permissions to the connection's service account: Project IAM Admin (roles/resourcemanager.projectIamAdmin)

• Create BigQuery jobs: BigQuery Job User (roles/bigquery.jobUser)

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:

• Create a dataset: bigquery.datasets.create
• Create, delegate, and use a connection: bigquery.connections.*
• Set service account permissions: resourcemanager.projects.getIamPolicy and resourcemanager.projects.setIamPolicy
• Create an object table: bigquery.tables.create and bigquery.tables.update
• Create a model and run inference:
  • bigquery.jobs.create
  • bigquery.models.create
  • bigquery.models.getData
  • bigquery.models.updateData
  • bigquery.models.updateMetadata

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

1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

2. 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

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

4. Enable the BigQuery, BigQuery Connection API, and Cloud Vision API APIs.

   Enable the APIs

5. 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

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

7. Enable the BigQuery, BigQuery Connection API, and Cloud Vision API APIs.

   Enable the APIs

Create a BigQuery dataset to contain your resources:

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:

   • For Dataset ID, type a name for the dataset.

   • For Location type, select a location for the dataset.

   • Click Create dataset.

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:

   • LOCATION: the dataset's location.
   • DATASET_ID is the ID of the dataset that you're creating.

2. Confirm that the dataset was created:

   bq ls

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:

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.

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:

   • REGION: your connection region
   • PROJECT_ID: your Google Cloud project ID
   • CONNECTION_ID: an ID for your connection

   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"}
   

Use the google_bigquery_connection resource.

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.

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.

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

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.

Select one of the following options:

1. Go to the IAM & Admin page.

   Go to IAM & Admin

2. Click person_add Add.

   The Add principals dialog opens.

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

4. In the Select a role field, select Service Usage, and then select Service Usage Consumer.

5. Click Add another role.

6. In the Select a role field, select BigQuery, and then select BigQuery Connection User.

7. Click Save.

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

gcloud projects add-iam-policy-binding 'PROJECT_NUMBER' --member='serviceAccount:MEMBER' --role='roles/serviceusage.serviceUsageConsumer' --condition=None
gcloud projects add-iam-policy-binding 'PROJECT_NUMBER' --member='serviceAccount:MEMBER' --role='roles/bigquery.connectionUser' --condition=None

Replace the following:

• PROJECT_NUMBER: your project number.
• MEMBER: the service account ID that you copied earlier.

Failure to grant the permission results in an error.

Create an object table that has image contents. The object table makes it possible to analyze the images without moving them from Cloud Storage.

The Cloud Storage bucket used by the object table should be in the same project where you plan to create the model and call the ML.ANNOTATE_IMAGE function. If you want to call the ML.ANNOTATE_IMAGE function in a different project than the one that contains the Cloud Storage bucket used by the object table, you must grant the Storage Admin role at the bucket level.

Create a remote model with a REMOTE_SERVICE_TYPE of CLOUD_AI_VISION_V1:

CREATE OR REPLACE MODEL
`PROJECT_ID.DATASET_ID.MODEL_NAME`
REMOTE WITH CONNECTION {DEFAULT | `PROJECT_ID.REGION.CONNECTION_ID`}
OPTIONS (REMOTE_SERVICE_TYPE = 'CLOUD_AI_VISION_V1');

Replace the following:

• PROJECT_ID: your project ID.
• DATASET_ID: the ID of the dataset to contain the model. This dataset must be in the same location as the connection that you are using.
• MODEL_NAME: the name of the model.
• REGION: the region used by the connection.
• CONNECTION_ID: the connection ID—for example, myconnection.

  When you view the connection details in the Google Cloud console, the connection ID is the value in the last section of the fully qualified connection ID that is shown in Connection ID—for example projects/myproject/locations/connection_location/connections/myconnection.

Annotate images with the ML.ANNOTATE_IMAGE function:

SELECT *
FROM ML.ANNOTATE_IMAGE(
  MODEL `PROJECT_ID.DATASET_ID.MODEL_NAME`,
  TABLE PROJECT_ID.DATASET_ID.OBJECT_TABLE_NAME,
  STRUCT(['FEATURE_NAME' [,...]] AS vision_features)
);

Replace the following:

• PROJECT_ID: your project ID.
• DATASET_ID: the ID of the dataset that contains the model.
• MODEL_NAME: the name of the model.
• OBJECT_TABLE_NAME: the name of the object table that contains the URIs of the images to annotate.
• FEATURE_NAME: the name of a supported Cloud Vision API feature.

Example 1

The following example labels the items shown in the images:

SELECT *
FROM ML.ANNOTATE_IMAGE(
  MODEL `myproject.mydataset.myvisionmodel`,
  TABLE myproject.mydataset.image_table,
  STRUCT(['label_detection'] AS vision_features)
);

Example 2

The following example detects any faces shown in the images, and also returns image attributes, like dominant colors:

SELECT *
FROM ML.ANNOTATE_IMAGE(
  MODEL `myproject.mydataset.myvisionmodel`,
  TABLE myproject.mydataset.image_table,
  STRUCT(['face_detection', 'image_properties'] AS vision_features)
);

• To learn more about model inference, including other functions you can use to analyze BigQuery data, see Model inference overview.
• For information about the supported SQL statements and functions for each model type, see End-to-end user journey for each model.
• Try the Unstructured data analytics with BigQuery ML and Vertex AI pre-trained models notebook.

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."],[[["This document guides you through using the `ML.ANNOTATE_IMAGE` function with a remote model to analyze images stored in an object table within BigQuery."],["Creating a cloud resource connection is necessary, which includes retrieving the connection's service account ID and properly configuring permissions such as `roles/bigquery.connectionAdmin`, and `resourcemanager.projects.setIamPolicy` to enable model creation and inference."],["You'll need to set up an object table containing the images and create a remote model using the `REMOTE_SERVICE_TYPE` of `CLOUD_AI_VISION_V1`, specifying the project, dataset, model name, and connection details."],["Annotate images by calling the `ML.ANNOTATE_IMAGE` function with the model, object table, and specified Cloud Vision API features, such as `label_detection`, `face_detection`, and `image_properties`."],["To grant access to the service account, go to the IAM & Admin page, add the service account id, and set the roles to 'Service Usage Consumer' and 'BigQuery Connection User'."]]],[]]

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4