Datasheet for Datasets

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.

Tracking where data came from and how it was transformed; key for debugging and compliance.

Time from request to response; critical for real-time inference and UX.

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.

Processes and controls for data quality, access, lineage, retention, and compliance across the AI lifecycle.

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