Model Card Toolkit

The Model Card Toolkit (MCT) is a technical framework designed to streamline the creation of Model Cards—standardized documentation that provides essential information about a machine learning model's performance, limitations, and intended use cases. As we approach 2026, the MCT has evolved into a critical component for enterprises navigating the regulatory landscapes of the EU AI Act and similar global mandates. Built on the foundations of the 'Model Cards for Model Reporting' research, the toolkit integrates natively with TensorFlow Extended (TFX) and ML Metadata (MLMD) to programmatically extract model lineage, evaluation metrics, and training data distributions. This automation reduces the friction of manual documentation while ensuring that the data presented is verified directly from the source code and artifact stores. In the current market, it serves as the 'AI Bill of Materials' (SBOM) equivalent, allowing solutions architects to bridge the gap between data science teams and legal compliance officers. Its architecture supports highly customizable Jinja2 templates, enabling organizations to export technical findings into human-readable HTML or machine-readable JSON formats suitable for automated auditing workflows.