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DataFreemiumOpen Source
HOPSWORKS
AI platform with a feature store for production ML workflows
AGPL-3.0
ABOUT
Machine learning teams often recompute features for each training run, leading to inconsistent feature definitions between training and serving and wasted computation. Hopsworks solves this with a feature store that centralizes feature engineering, versioning, and serving — providing both online (low-latency) and offline (batch) access to features, ensuring consistency across training and inference pipelines.
INSTALL
pip install hopsworksINTEGRATION GUIDE
1. Centralize feature engineering for a team of data scientists with shared, versioned feature definitions
2. Build a training-to-serving pipeline that uses the same features for model training and real-time inference
3. Implement feature backfill for historical training runs with point-in-time correct feature retrieval
4. Create a feature monitoring system that tracks feature drift and data quality across production models
5. Power an ML platform where multiple teams share and discover features through a feature registry
TAGS
feature-storemlopsdata-platformfeature-engineeringmodel-servingpythonenterprise