The evolution of Feature Stores [PyCon DE & PyData Berlin 2024]

Feature stores emerged from the need to avoid duplicating data transformations and improve data discovery across teams and departments

Key capabilities of feature stores include:

• Lineage tracking for debugging and compliance
• Fast serving layer for real-time applications
• Point-in-time lookups and backfilling
• Feature discovery and sharing across teams
• Batch and streaming data processing

Feature store evolution phases:

• Phase 0 (2003-2009): Basic data storage and caching
• Phase 1 (2010-2016): Big data processing tools integration
• Current phase: Focus on governance, compliance and cross-team collaboration

Main differences between feature stores and databases:

• Purpose-built for ML workflows
• Built-in lineage tracking
• Optimized for both offline training and online serving
• Feature-level access controls and documentation

When to consider using a feature store:

• Large number of data scientists/ML engineers
• Need for feature reuse across teams
• Requirements for fast online serving
• Complex compliance and governance needs
• Multiple ML models using shared features

Organizational considerations:

• Company size influences feature store needs
• Maintenance overhead must be justified
• Alignment with existing data infrastructure
• Team collaboration requirements
• Compliance and governance requirements