Artificial Intelligence (AI) is poised to drive the next wave of technological disruption across nearly every industry.
Neo4j customers are demonstrating that graph database technology brings tremendous value to AI and machine learning projects – especially in the area of knowledge graphs, which add essential context for AI applications.
How Graph Technology is Changing Artificial Intelligence and Analytics
Neo4j's Amy Hodler and Jake Graham, speaking at GraphConnect 2018, talk about how using graph technologies in AI and Analytics applications improves accuracy, modeling speed and accessibility.Watch Video
Neo4j & Expero: Lower Risk & Stop Fraud Using Graph-Enhanced Machine Learning & AI
Understand how successful financial services, banks and retailers are using graph technology and embedding intelligence to quickly identify risk and fraud patterns as they evolve.Watch video
eBay’s App Delivers Recommendations with Artificial Intelligence & Neo4j
eBay’s Chief Product Officer explains how he turned to graph technology because existing product searches and recommendation engines were unable to provide contextual information within a shopping request.Read more
Whether your AI solution is chatting with customers or driving an autonomous vehicle, real-time feedback loops are essential for meaningful machine learning that evolves your intelligence models.
Evolves with business requirements
In the emergent industry of AI, business and user requirements are still being defined, tweaked or completely upended. The graph data model is more agile and flexible than a traditional RDBMS in meeting these new and changing requirements.
Disparate (and changing) data sources
Your machine learning algorithms consume data from a variety of ever-changing sources and data types, meaning you need a database with a versatile and adaptable schema.
Multiple hop queries
In order to determine context for the most appropriate action, AI solutions must query several layers deeper within their databases than previous technologies required. This means the database layer must be able to support multi-hop (4+) queries without affecting performance.
Native graph store
Neo4j natively stores interconnected data – neither linear nor purely hierarchical – so it’s easier to decipher your data.
Our versatile property graph model makes it easier for organization to evolve machine learning and AI models.
Performance and scalability
Neo4j supports high-performance graph queries on large datasets enabling real-time AI solutions.
Popular Graph Technology Use Cases for AI and Analytics
Pre-register for the new O'Reilly book on Graph Algorithms
Whether you are trying to build dynamic network models or forecast real-world behavior, this book illustrates how graph algorithms deliver value – from finding vulnerabilities and bottlenecks to detecting communities and improving machine learning predictions.