Every model I deploy now gets a model card. No exceptions.

A model card is a one-page document that answers: What does this model do? What data was it trained on? What are its limitations? How well does it perform across different groups? When should you NOT use it?

It sounds like bureaucratic overhead. It's actually the most useful piece of documentation in the entire ML lifecycle.

When a new team member needs to understand the model: model card. When a stakeholder asks about bias or fairness: model card. When you need to debug unexpected behavior 8 months later: model card. When a regulator asks about your AI system: model card.

What goes in mine: model description and intended use, training data description and known gaps, performance metrics broken down by relevant segments, known limitations and failure modes, ethical considerations, and contact info for the model owner.

Template: Hugging Face model cards are a great starting point. Customize for your organization's needs.

The EU AI Act is making documentation requirements explicit. But even without regulation, a model card saves you hours of "wait, what does this model actually do?" conversations.

One hour of documentation now saves twenty hours of confusion later. Write the model card.