ML Model Card Template for AI Products

A model card is a standardized document that describes a machine learning model's purpose, training data, performance characteristics, limitations, and ethical considerations. Think of it as a nutrition label for ML models. It helps stakeholders understand what the model can and cannot do before they rely on it.

The ML engineer or data scientist who built the model drafts the technical sections. The product manager adds the intended use, limitations framing, and ethical considerations. Legal or compliance reviews the final document. Ownership typically sits with the ML team, but the PM ensures it is written for a cross-functional audience.

Update the model card whenever you retrain the model, discover a new failure mode, change the training data, or expand the model's use cases. At minimum, review model cards quarterly even if no changes have been made. Stale model cards are worse than no model card because they create false confidence.

Break performance down by every segment that matters for your use case. If your model serves different customer tiers, show metrics per tier. If it processes multiple languages, show per-language metrics. The goal is to surface performance gaps that aggregate numbers hide. The [model accuracy score](/metrics/model-accuracy-score) metric provides guidance on measurement methodology.

An AI PRD defines what you want to build before development starts. A model card documents what you actually built after the model is trained. The PRD is a planning artifact. The model card is an accountability artifact. Both are necessary. Use the [AI Product PRD Template](/templates/ai-product-prd-template) for the planning phase. ---