Use cases
Model documentation is required for many companies and data science teams. Model documentation can include information about how a model was created, trained, tested, and compared to alternative models. Collecting and documenting this information per model can take several days because the model document needs to be comprehensive. As a result, creating this documentation is tedious for data scientists and wasteful for businesses. Additionally, inconsistent or inaccurate model documentation can be an issue for model validation, governance, and regulatory compliance. H2O AutoDoc is designed to solve this problem by letting users create comprehensive automatic documentation fast. Below explore available use cases.
H2O Driverless AI, H2O-3, and Scikit-Learn
Supervised learning models built in H2O Driverless AI, H2O-3, and Scikit-Learn can obtain automatic documentation through H2O AutoDocs.
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