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Feature store

Central infrastructure that defines each feature once, computes it consistently for training and live serving, and tracks which models use it. Its lineage tracking lets teams see, when a feature drifts, exactly which (and how critical) the affected models are.

Defined in 2 GAGE programs, which carry 4 distinct definitions of it. The wording above is taught in Business AI Transformation.

How each discipline defines it

The same term does different work depending on who is using it. These are the definitions as each program teaches them, unedited.

Business AI Transformation

Central infrastructure that defines each feature once, computes it consistently for training and live serving, and tracks which models use it. Its lineage tracking lets teams see, when a feature drifts, exactly which (and how critical) the affected models are.

AI Data Governance: The Data Chair

a governed, versioned, searchable system that defines, computes, stores, and serves machine learning features consistently across model training and live production use.

Business AI Transformation

A central repository of AI-ready features that serves both model training (historical values) and inference (current values), ensuring the features used in training match those used in production and can be reused across models.

Business AI Transformation

a governed place where pre-computed data features for AI models are stored, versioned, and reused across models, so the same clean inputs serve both training and live inference.

Where it is taught

The exact lessons this term appears in. The first 7 topics of every program are free with a free account.

Terms it appears with

Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.