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 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.
“More software developers are looking to data science for ways to offer better predictions and stronger recommendation engines to users. Google Cloud and Neo4j Graph Data Science products help software developers and data scientists who are building the world’s next set of intelligent applications by leveraging the power of graph algorithms to bring context to data and improve their models,” said Suri.
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.
According to Zack Gow, CTO of Orita, Neo4j Graph Data Science has enabled his team to be more responsive to customer needs.
“Scale is always top of mind for us because we’re processing data that comes from our customers. We never know just how big a customer’s data set will be and we chose Neo4j because we knew it could handle the scaling of an order of magnitude more than what we were expecting,” Gow said. “Even in the early days, when we were trying out a bunch of tools, Neo4j worked for us immediately. Some of the tools we looked at didn’t work at all. Neo4j Graph Data Science got our data into a graph so we could start doing the data science part quickly. As a start up, we don’t have time to waste on tools that are cumbersome.”
Matthew Bernardini, CEO of Zenapse, shared the impact of Neo4j Graph Data Science on his business.
“We chose Neo4j Graph Data Science on AuraDS because it is a completely managed, cloud-based infrastructure combined with an elegant and user-friendly set of tools and extensive library of production-ready data science algorithms that gives us confidence in our platform and allows us to focus on our data and application development,” said Bernardini. “Neo4j Graph Data Science makes it easy to quantify the relationships and similarities that exist in the digital world and to surface new insights about these connected relationships.”
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.
