Meet Feast: An Open Source Feature Store for Machine Learning

Meet Feast: An Open Source Feature Store for Machine Learning

A feature store is a central repository for storing, processing, and accessing commonly used features in a machine learning (ML) workflow. Such a repository ensures reproducibility, maintains model performance, enhances security and data governance, and fosters collaboration. Feast is an open-source feature store that helps organizations store and serve features for offline training and online inference.

Users can connect with stream and batch data sources such as Kafka, Snowflake, Redshift, S3, GCS, etc., and apply transformations to create features that can be easily stored and served for real-time model inference and model training. Feast also provides other features, which are mentioned below.

  • Users can use ETL/ELT systems like Spark and SQL to transform the data.
  • Stream features can be created from services like Kafka or Kinesis and pushed directly into Feast.
  • Users can publish versioned controlled feature definitions and load features from offline to the online store.
  • Feast also allows users to get historical features.
  • Users can also launch a model training pipeline, deploy the model, and get real-time predictions.


Advantages of Feast

  • Feast is an open-source feature store that can be easily used via Python.
  • Feast supports both offline and online feature stores.
  • Feast helps ML platform teams produce real-time models and fosters collaboration between engineers and data scientists.
  • Feast makes the features continuously available for training and service.
  • It generates accurate feature sets that are point-in-time correct, which helps avoid data leakage.
  • It provides a single data access layer that decouples ML from data infrastructure and ensures the portability of models.
  • Feast can power multiple models simultaneously with new and reusable features on demand.

Limitations of Feast

  • Feast does not version control datasets or manage train-test splits. Tools like DVC and MLflow are better suited for these tasks.
  • Users can push streaming features to Feast but cannot pull them from the platform.
  • Feast is not suited for organizations relying primarily on unstructured data.
  • Feast mainly processes feature values that have already been processed.
  • The platform does not focus on solving data drift or data quality issues.

In conclusion, Feast is an open-source feature store that helps organizations build real-time models that can be easily deployed and monitored. Many companies are leveraging Feast in applications like personalized online recommendations, churn prediction, and fraud detection applications using the platform's capabilities. 

The platform has a few limitations as well. It does not fully solve requirements like experiment management, streaming feature engineering, feature sharing, and drift detection and only has experimental functionalities for some of these. Therefore, other tools like DVC, MLflow, or Tecton can more robustly address these needs, and users should choose the appropriate tool based on their requirements.

About the author
Manya Goyal

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