New Supervised Machine Learning Workflows in Neo4j!

The Neo4j Graph Data Science (GDS) Library has released its version 1.5, which brings several enhancements and new features, including graph embeddings, supervised machine learning, and easier production deployment. This release allows users to create an end-to-end model-building pipeline within Neo4j, enabling advanced ML techniques such as node classification and link prediction. Graph-native feature engineering is also introduced, allowing users to explore their data using graph algorithms and generate high-quality features for predictive models. The new release includes performance improvements and a low-memory format optimized for large graphs. Users can train supervised machine learning models, persist and share them, and manage them using the Neo4j model catalog. This update makes it possible for companies outside of Big Tech to take advantage of advanced graph-based ML techniques, marking a significant milestone in the field of graph data science.

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