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The new DSaaS offering “accelerates development of intelligent apps,” the company says.

This week Neo4j announced Neo4j Graph Data Science, the company’s comprehensive graph analytics workspace built for data scientists, 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.

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 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.

Neo4j AuraDS: Graph Data Science on Google Cloud Platform

Neo4j AuraDS is the power of Graph Data Science available as a fully managed service. It includes access to over 65 graph algorithms in a single workspace so data scientists can experiment faster. In-graph ML models and the native Python client help increase productivity and simplify workflows. 

Neo4j AuraDS is available first on Google Cloud’s secure, global, and highly performant structure, and can be paid for with existing Google Cloud commitments or with a credit card. In addition to the Graph Data Science core functionality, AuraDS customers benefit from:

  • Simple, powerful workflow: A drag-and-drop UI to model and import data into a graph.
  • Scale up and down: Manage access to high compute hardware on-demand as needs change.
  • Automated operations: Workloads are monitored, patched, and backed up behind the scenes without any user action. 
  • MLOps support: Persist, publish, and restore models without interruptions from restarts. 
  • Predictable cost: Manage costs with pay-as-you-go pricing and the option of pausing unused instances.
  • One-click backup: Take a snapshot of instances, models, and in-memory graphs in one click.

Also read: Snowflake introduces data platform for healthcare.