Developed comprehensive documentation for the ArangoGraphML UI within the arangodb/docs-hugo repository, focusing on guiding users through node classification and node embeddings workflows. The work detailed the application of GraphSAGE-based featurization and training processes, providing clear explanations of underlying machine learning concepts. Using Markdown and technical writing skills, the documentation included step-by-step guides for creating, configuring, and executing GraphML projects, as well as model selection and prediction. Limitations and best practices were thoroughly documented to help users set realistic expectations. This structured reference material aimed to streamline onboarding, accelerate adoption, and reduce support needs for the GraphML feature set.