Get model metadata

This page shows you how to get information or metadata about BigQuery ML models. You can get model metadata by:

Using the Google Cloud console
Using the bq show CLI command
Calling the models.get API method directly or by using the client libraries

Required permissions

To get model metadata, you must be assigned the READER role on the dataset, or you must be assigned a project-level Identity and Access Management (IAM) role that includes bigquery.models.getMetadata permissions. If you are granted bigquery.models.getMetadata permissions at the project level, you can get metadata on models in any dataset in the project. The following predefined, project-level IAM roles include bigquery.models.getMetadata permissions:

bigquery.dataViewer
bigquery.dataEditor
bigquery.dataOwner
bigquery.metadataViewer
bigquery.admin

For more information on IAM roles and permissions in BigQuery ML, see Access control.

To get metadata about models:

Console

In the left pane, click Explorer:

If you don't see the left pane, click Expand left pane to open the pane.
In the Explorer pane, expand the project, click Datasets, and then select the dataset.
Click the Models tab, and then click a model name to select the model.
Click the Details tab. This tab displays the model's metadata, including the description, labels, model type, and training options.

bq

Issue the bq show command with the --model or -m flag to display model metadata. The --format flag can be used to control the output.

To see only the feature columns for your model, use the --schema flag with the --model flag. When you use the --schema flag, --format must be set to either json or prettyjson.

If you are getting information about a model in a project other than your default project, add the project ID to the dataset in the following format: [PROJECT_ID]:[DATASET].

bq show --model --format=prettyjson PROJECT_ID:DATASET.MODEL

Replace the following:

PROJECT_ID is your project ID.
DATASET is the name of the dataset.
MODEL is the name of the model.

The command output looks like the following when the --format=pretty flag is used. To see full details, use the --format=prettyjson format. The sample output shows metadata for a logistic regression model.

+--------------+---------------------+---------------------+---------------------------+--------+-----------------+-----------------+
|      Id      |     Model Type      |   Feature Columns   |       Label Columns       | Labels |  Creation Time  | Expiration Time |
+--------------+---------------------+---------------------+---------------------------+--------+-----------------+-----------------+
| sample_model | LOGISTIC_REGRESSION | |- column1: string  | |- label_column: int64    |        | 03 May 23:14:42 |                 |
|              |                     | |- column2: bool    |                           |        |                 |                 |
|              |                     | |- column3: string  |                           |        |                 |                 |
|              |                     | |- column4: int64   |                           |        |                 |                 |
+--------------+---------------------+---------------------+---------------------------+--------+-----------------+-----------------+

Examples:

Enter the following command to display all information about mymodel in mydataset. mydataset is in your default project.

bq show --model --format=prettyjson mydataset.mymodel

Enter the following command to display all information about mymodel in mydataset. mydataset is in myotherproject, not your default project.

bq show --model --format=prettyjson myotherproject:mydataset.mymodel

Enter the following command to display only the feature columns for mymodel in mydataset. mydataset is in myotherproject, not your default project.

bq show --model --schema --format=prettyjson \
myotherproject:mydataset.mymodel

API

To get model metadata by using the API, call the models.get method and provide the projectId, datasetId, and modelId.

Go

Before trying this sample, follow the Go setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery Go API reference documentation.

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

Java

Before trying this sample, follow the Java setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery Java API reference documentation.

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

Node.js

Before trying this sample, follow the Node.js setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery Node.js API reference documentation.

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

Python

Before trying this sample, follow the Python setup instructions in the BigQuery quickstart using client libraries. For more information, see the BigQuery Python API reference documentation.

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

What's next

For an overview of BigQuery ML, see Introduction to BigQuery ML.
To get started using BigQuery ML, see Create machine learning models in BigQuery ML.
To learn more about working with models, see: