Machine learning models are now being used to accomplish many challenging tasks. With their vast potential, ML models also raise questions about their usage, construction, and limitations. Documenting the answers to these questions helps to bring clarity and shared understanding. To help advance these goals, Google has introduced model cards.

Model cards aim to provide a concise, holistic picture of a machine learning model. To start, a model card explains what a model does, its intended audience, and who maintains it. A model card also provides insight into the construction of the model, including its architecture and the training data used. Not only does a model card include raw performance metrics-- it puts a model’s limitations and risk mitigation opportunities into context. The Model Cards for Model Reporting research paper provides detailed coverage of model cards.

In this blog post, I hope to show how easy it is for you to create your own model card. We will use the popular scikit-learn framework, but the concepts you learn here will apply whether you’re using TensorFlow, PyTorch, XGBoost, or any other framework.

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