Integrate Feast with ZenML - Feature Store Integrations

Seamlessly Integrate Feature Stores into ML Pipelines with Feast and ZenML

Enhance your machine learning workflows by integrating Feast, a powerful feature store, with ZenML. This integration enables efficient management and serving of features for model training and inference, streamlining the ML pipeline process.

Features with ZenML

Seamless integration of Feast feature stores into ZenML pipelines
Access to historical feature data for model training
Simplified feature retrieval and management within ML workflows
Improved collaboration and reproducibility across teams

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Main Features

Unified serving of features to models for training and inference
Scalable offline store for batch scoring and model training
Integration with various data sources and platforms
Centralized feature registry and versioning

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How to use ZenML with Feast


# First register the feature store and a stack
# zenml feature-store register feast_store --flavor=feast --feast_repo="<PATH/TO/FEAST/REPO>"
# zenml stack register ... -f feast_store

from zenml import step, pipeline
from zenml.client import Client

@step
def get_historical_features(
    entity_dict, features, full_feature_names=False
):
    feature_store = Client().active_stack.feature_store
    entity_df = pd.DataFrame.from_dict(entity_dict)

    return feature_store.get_historical_features(
        entity_df=entity_df,
        features=features,
        full_feature_names=full_feature_names,
    )

@pipeline
def my_pipeline():
    my_features = get_historical_features(
        entity_dict={"driver_id": [1001, 1002, 1003]},
        features=["driver_hourly_stats:conv_rate", "driver_hourly_stats:acc_rate"]
    )
    # use features in downstream steps

# also use the CLI for Feast metadata etc
# zenml feature-store feature get-entities
# zenml feature-store feature get-data-sources
# ...

Additional Resources

Feast Official Documentation Blog Post: “Building an Efficient and Scalable MLOps Workflow with Feast and ZenML”