Amazon Neptune ML is a machine learning (ML) capability of Amazon Neptune that helps you make accurate and fast predictions on your graph data. Under the hood, Neptune ML uses Graph Neural Networks (GNNs) to simultaneously take advantage of graph structure and node/edge properties to solve the task at hand. Traditional methods either only use properties and no graph structure (e.g., XGBoost, Neural Networks), or only graph structure and no properties (e.g., node2vec, Label Propagation). To better manipulate the node/edge properties, ML algorithms require the data to be well behaved numerical data, but raw data in a database can have other types, like raw text. To make use of these other types of data, we need specialized processing steps that convert them from their native type into numerical data, and the quality of the ML results is strongly dependent on the quality of these data transformations. Raw text, like sentences,

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