Reproducibility

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.

Hardware resources used for training/inference; constrained by memory bandwidth, FLOPs, and parallelism.

Techniques that fine-tune small additional components rather than all weights to reduce compute and storage.

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.

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

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