This feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of theService Specific Terms. Pre-GA features are available "as is" and might have limited support. For more information, see thelaunch stage descriptions.

If you need to retrieve or serve current as well as historicalfeature data, use offline serving to fetch feature values fromBigQuery. For example, you can use offline serving to retrievethe feature values for specific timestamps to train a model.

All feature data, including historical feature data, is maintained inBigQuery, which constitutes the offline store for your feature values.To use offline serving, you must first register your BigQuerydata source by creating feature groups and feature values. Also, in the case ofoffline serving, every row containing the same entity ID must have adifferent timestamp. For more information about data sourcepreparation guidelines, seePrepare data source.

Before you begin

Authenticate toVertex AI, unless you've done so already.

To use the Python samples on this page in a local development environment, install and initialize the gcloud CLI, and then set up Application Default Credentials with your user credentials.

Install the Google Cloud CLI.

If you're using an external identity provider (IdP), you must first sign in to the gcloud CLI with your federated identity.

If you're using a local shell, then create local authentication credentials for your user account:

gcloudauthapplication-defaultlogin

You don't need to do this if you're using Cloud Shell.

If an authentication error is returned, and you are using an external identity provider (IdP), confirm that you have signed in to the gcloud CLI with your federated identity.

For more information, see Set up authentication for a local development environment.

Fetch historical feature values

Use the following sample to fetch historical values from a feature from multipleentity IDs and timestamps.

importbigframesimportbigframes.pandasimportpandasaspdfromgoogle.cloudimportbigqueryfromvertexai.resources.preview.feature_storeimport(Feature,FeatureGroup,offline_store)fromvertexai.resources.preview.feature_storeimportutilsasfs_utilsfg=FeatureGroup("FEATURE_GROUP_NAME")f1=fg.get_feature("FEATURE_NAME_1")f2=fg.get_feature("FEATURE_NAME_2")entity_df=pd.DataFrame(data={"ENTITY_ID_COLUMN":["ENTITY_ID_1","ENTITY_ID_2",],"timestamp":[pd.Timestamp("FEATURE_TIMESTAMP_1"),pd.Timestamp("FEATURE_TIMESTAMP_2"),],},)offline_store.fetch_historical_feature_values(entity_df=entity_df,features=[f1,f2],)