Feature Stores

A feature store enables an offline and online serving of feature data.

Feature stores allow data teams to serve data via an offline store and an online low-latency store where data is kept in sync between the two. It also offers a centralized registry where features (and feature schemas) are stored for use within a team or wider organization.

As a data scientist working on training your model, your requirements for how you access your batch / 'offline' data will almost certainly be different from how you access that data as part of a real-time or online inference setting. Feast solves the problem of developing train-serve skew where those two sources of data diverge from each other.

BaseFeatureStore

Bases: StackComponent, ABC

Base class for all ZenML feature stores.

Source code in src/zenml/feature_stores/base_feature_store.py

class BaseFeatureStore(StackComponent, ABC):
    """Base class for all ZenML feature stores."""

    @property
    def config(self) -> BaseFeatureStoreConfig:
        """Returns the `BaseFeatureStoreConfig` config.

        Returns:
            The configuration.
        """
        return cast(BaseFeatureStoreConfig, self._config)

    @abstractmethod
    def get_historical_features(
        self,
        entity_df: Any,
        features: List[str],
        full_feature_names: bool = False,
    ) -> Any:
        """Returns the historical features for training or batch scoring.

        Args:
            entity_df: The entity DataFrame or entity name.
            features: The features to retrieve.
            full_feature_names: Whether to return the full feature names.

        Returns:
            The historical features.
        """

    @abstractmethod
    def get_online_features(
        self,
        entity_rows: List[Dict[str, Any]],
        features: List[str],
        full_feature_names: bool = False,
    ) -> Dict[str, Any]:
        """Returns the latest online feature data.

        Args:
            entity_rows: The entity rows to retrieve.
            features: The features to retrieve.
            full_feature_names: Whether to return the full feature names.

        Returns:
            The latest online feature data as a dictionary.
        """

config property

Returns the BaseFeatureStoreConfig config.

Returns:

Type	Description
BaseFeatureStoreConfig	The configuration.

get_historical_features(entity_df, features, full_feature_names=False) abstractmethod

Returns the historical features for training or batch scoring.

Parameters:

Name	Type	Description	Default
entity_df	Any	The entity DataFrame or entity name.	required
features	List[str]	The features to retrieve.	required
full_feature_names	bool	Whether to return the full feature names.	False

Returns:

Type	Description
Any	The historical features.

Source code in src/zenml/feature_stores/base_feature_store.py

@abstractmethod
def get_historical_features(
    self,
    entity_df: Any,
    features: List[str],
    full_feature_names: bool = False,
) -> Any:
    """Returns the historical features for training or batch scoring.

    Args:
        entity_df: The entity DataFrame or entity name.
        features: The features to retrieve.
        full_feature_names: Whether to return the full feature names.

    Returns:
        The historical features.
    """

get_online_features(entity_rows, features, full_feature_names=False) abstractmethod

Returns the latest online feature data.

Parameters:

Name	Type	Description	Default
entity_rows	List[Dict[str, Any]]	The entity rows to retrieve.	required
features	List[str]	The features to retrieve.	required
full_feature_names	bool	Whether to return the full feature names.	False

Returns:

Type	Description
Dict[str, Any]	The latest online feature data as a dictionary.

Source code in src/zenml/feature_stores/base_feature_store.py

@abstractmethod
def get_online_features(
    self,
    entity_rows: List[Dict[str, Any]],
    features: List[str],
    full_feature_names: bool = False,
) -> Dict[str, Any]:
    """Returns the latest online feature data.

    Args:
        entity_rows: The entity rows to retrieve.
        features: The features to retrieve.
        full_feature_names: Whether to return the full feature names.

    Returns:
        The latest online feature data as a dictionary.
    """