Data Governance

Standardized documentation describing intended use, performance, limitations, data, and ethical considerations.

Structured dataset documentation covering collection, composition, recommended uses, biases, and maintenance.

Central system to store model versions, metadata, approvals, and deployment state.

Ability to replicate results given same code/data; harder in distributed training and nondeterministic ops.

Expanding training data via transformations (flips, noise, paraphrases) to improve robustness.

Artificially created data used to train/test models; helpful for privacy and coverage, risky if unrealistic.

Training across many devices/silos without centralizing raw data; aggregates updates, not data.

Information that can identify an individual (directly or indirectly); requires careful handling and compliance.