What practices are used when synchronizing training-serving features?

When synchronizing training and serving features, it is important to ensure that the model receives the same data during inference as during training to avoid drift and inconsistencies. Key practices:

• Single source of truth for features: use a common feature store where features for training and serving are stored and updated.
• Identical transformations: apply the same transformations to data in both modes.
• Feature versioning: fix feature versions to reproduce training and inference with the same data.
• Automation of updates: set up pipelines that automatically update features and synchronize them between training and serving.

Example: using systems like Feast or Tecton, which ensure data consistency between training and production.