'Hopsworks 3.0: The Python-Centric Feature Store' machinelearning python
Introduction
Feature stores began in the world of Big Data, with Spark being the feature engineering platform for Michelangelo and Hopsworks . Nowadays, the modern data stack has assumed the role of Spark for feature stores - feature engineering code can be written that seamlessly scales to large data volumes in Snowflake, BigQuery, or Redshift. However, Python developers know that feature engineering is so much more than the aggregations and data validation you can do in SQL and DBT.
Built-in or custom transformation functions can be attached to a feature view to enable untransformed data to be stored in the Feature Store, important for reusability of features, EDA, and consistent transformations to be applied across training and inference data read from a Feature View. The training data created from a Feature View is immutable - a reproducible snapshot of the features you selected that includes a time range and any filters you apply .
Hopsworks also enables A/B testing of features. Just as you can deploy a new model to improve your prediction performance, you can also deploy new features to improve your prediction performance. This is commonly known as data-centric AI. Hopsworks supports A/B testing of features through comprehensive versioning support for schemas and data. From raw data to model inputs, Hopsworks manages versioned schemas of Feature Groups, and Feature Views - the inputs to models.
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