Serve historical feature values

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

All feature data, including historical feature data, is maintained in BigQuery, which constitutes the offline store for your feature values. To use offline serving, you must first register your BigQuery data source by creating feature groups and feature values. Also, in the case of offline serving, every row containing the same entity ID must have a different timestamp. For more information about data source preparation guidelines, see Prepare data source.

Before you begin

Authenticate to Vertex 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:

gcloud auth application-default login

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 multiple entity IDs and timestamps.

import bigframes
import bigframes.pandas
import pandas as pd
from google.cloud import bigquery
from vertexai.resources.preview.feature_store import (Feature, FeatureGroup, offline_store)
from vertexai.resources.preview.feature_store import utils as fs_utils

fg = 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],
)