Compute features on demand using Python user-defined functions

Preview

Machine learning models for real-time applications often require the most recent feature values. For example, a recommender model might need information about the user’s current situation, such as their distance from a store or restaurant, or the total value of items in their cart. These feature values must be computed at the time of inference.

To use on-demand features, your workspace must be enabled for Unity Catalog and you must use Databricks Runtime 13.3 LTS ML or above.

What are on-demand features?

“On-demand” refers to features whose values are not known ahead of time, but are calculated at the time of inference. In Databricks, you use Python user-defined functions (UDFs) to specify how to calculate on-demand features. These functions are governed by Unity Catalog and discoverable through Catalog Explorer.

Workflow

To compute features on-demand, you specify a Python user-defined function (UDF) that describes how to calculate the feature values.

During training, you provide this function and its input bindings in the feature_lookups parameter of the create_training_set API.
You must log the trained model using the Feature Store method log_model. This ensures that the model automatically evaluates on-demand features when it is used for inference.
For batch scoring, the score_batch API automatically calculates and returns all feature values, including on-demand features.
When you serve a model with Databricks Model Serving, the model automatically uses the Python UDF to compute on-demand features for each scoring request.

Create a Python UDF

You can create a Python UDF in a notebook or in Databricks SQL.

For example, running the following code in a notebook cell creates the Python UDF example_feature in the catalog main and schema default.

%sql
CREATE FUNCTION main.default.example_feature(x INT, y INT)
RETURNS INT
LANGUAGE PYTHON
COMMENT 'add two numbers'
AS $$
def add_numbers(n1: int, n2: int) -> int
  return n1 + n2

return add_numbers(x, y)
$$

After running the code, you can navigate through the three-level namespace in Catalog Explorer to view the function definition:

For more details about creating Python UDFs, see Register a Python UDF to Unity Catalog and the SQL language manual.

Train a model using on-demand features

To train the model, you use a FeatureFunction, which is passed to the create_training_set API in the feature_lookups parameter.

The following example code uses the Python UDF main.default.example_feature that was defined in the previous section.

from databricks.feature_store import FeatureFunction, FeatureLookup
from databricks.feature_store.client import FeatureStoreClient
from sklearn import linear_model

fs = FeatureStoreClient()

features = [
  # The feature 'on_demand_feature' is computed as the sum of the the input value 'new_source_input'
  # and the pre-materialized feature 'materialized_feature_value'.
  # - 'new_source_input' must be included in base_df and also provided at inference time.
  #   - For batch inference, it must be included in the DataFrame passed to 'FeatureStoreClient.score_batch'.
  #   - For real-time inference, it must be included in the request.
  # - 'materialized_feature_value' is looked up from a feature table.

  FeatureFunction(
      udf_name="main.default.example_feature",    # UDF must be in Unity Catalog so uses a three-level namespace
      input_bindings={
        "x": "new_source_input",
        "y": "materialized_feature_value"
      },
      output_name="on_demand_feature",
  ),
  # retrieve the prematerialized feature
  FeatureLookup(
    table_name = 'main.default.table',
    feature_names = ['materialized_feature_value'],
    lookup_key = 'id'
  )
]

# base_df includes the columns 'id', 'new_source_input', and 'label'
training_set = fs.create_training_set(
  base_df,
  feature_lookups=features,
  label='label',
  exclude_columns=['id', 'new_source_input', 'materialized_feature_value']     # drop the columns not used for training
)

# The training set contains the columns 'on_demand_feature' and 'label'.
training_df = training_set.load_df().toPandas()

# training_df columns ['materialized_feature_value', 'label']
X_train = training_df.drop(['label'], axis=1)
y_train = training_df.label

model = linear_model.LinearRegression().fit(X_train, y_train)

Log the model and register it to Unity Catalog

Models packaged with feature metadata can be registered to Unity Catalog. The feature tables used to create the model must be stored in Unity Catalog.

To ensure that the model automatically evaluates on-demand features when it is used for inference, you must set the registry URI and then log the model, as follows:

import mlflow
mlflow.set_registry_uri("databricks-uc")

fs.log_model(
    model,
    "main.default.model",
    flavor=mlflow.sklearn,
    training_set=training_set,
    registered_model_name="main.default.recommender_model"
)

If the Python UDF that defines the on-demand features imports any Python packages, you must specify these packages using the argument extra_pip_requirements. For example:

import mlflow
mlflow.set_registry_uri("databricks-uc")

fs.log_model(
    model,
    "model",
    flavor=mlflow.sklearn,
    training_set=training_set,
    registered_model_name="main.default.recommender_model",
    extra_pip_requirements=["scikit-learn==1.20.3"]
)

Limitation

On-demand features can output all data types supported by Feature Store except MapType and ArrayType.