Better AI With Neo4j and Azure Machine Learning

Neo4j is a graph database that enables the exploration of connected data, providing insights trapped in rows and columns. With over 70 Data Science Algorithms, Neo4j simplifies machine learning In-Graph, translating connected data into predictive signals through Graph embeddings. This integration with Azure Machine Learning (Azure ML) accelerates and manages the machine learning product lifecycle, enhancing a supervised learning model trained with Azure ML. By leveraging Neo4j's Graph Feature Engineering capabilities, Data Scientists can create in-memory graph projections and node embeddings using Fast RP, exporting them to Azure ML for an AutoML job. The integration enables the creation of an AutoML job to predict policy claims based on enriched data, providing insights into the model and top features by importance. This post introduces Neo4j's integration with Azure ML, showcasing its potential in graph problems across various industries.

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