Graph data platform Neo4j has announced Neo4j Graph Data Science, the company’s comprehensive graph analytics workspace built for data scientists. It is now available with new and enhanced capabilities, and as a fully managed cloud service called AuraDS.
AI and machine learning (ML) have propelled the use of predictive data architectures and their application across a broad range of use cases like recommendation engines, fraud detection, and customer 360 scenarios. The accuracy of these models is highly correlated to the completeness of context.
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Neo4j Graph Data Science is designed to make it easy for data scientists to achieve greater predictive accuracy with comprehensive graph analysis techniques. Users can improve models through a library of graph algorithms, ML pipelines, and data science methods. Neo4j Graph Data Science has been widely adopted, and is trusted to perform at scale, easily handling hundreds of billions of nodes and relationships.
“Neo4j’s Graph Data Science offerings help developers offer better predictions and stronger recommendation engines to business users,” said Ritika Suri, Director, Technology Partnerships at Google. “Customers can now deploy Graph Data Science on Google Cloud’s trusted, global infrastructure, gaining the ability to seamlessly scale based on business needs, and bringing their data closer to BigQuery and Google Cloud’s capability in AI, ML, and analytics.”
Neo4j Graph Data Science makes it easy for data scientists to work within their existing data pipeline of tools across their ecosystem. Data scientists can use Neo4j Graph Data Science on-premises, and now as a fully managed SaaS solution via Neo4j AuraDS.