model-card

Generate a standardized model card for ML model deployment handoff by documenting model details, intended use, training data, evaluation metrics, and ethical considerations. This ensures reproducibility and facilitates governance reviews across ML projects.

A model card is standardized documentation capturing an ML model's purpose, performance, limitations, and ethical considerations. You need one for deployment handoff, governance reviews, and ensuring reproducibility across ML projects to meet AI ethics standards.

Yes, the generated model documentation explicitly aligns with HuggingFace Model Card and NVIDIA Model Card++ formats. It includes standardized sections for model details, intended use, training data, metrics, limitations, and ethical considerations.

Document training data for ML model reproducibility by recording the data source, date range, size, data hash, and preprocessing steps. Capturing known biases ensures transparency and ethical compliance during governance reviews.

For deployed ML models, document out-of-scope uses within the intended use section and record model limitations separately. Capturing these boundaries ensures ethical deployment, informs users, and supports governance compliance.