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.
Read this white paper, Artificial Intelligence & Graph Technology: Enhancing AI with Context & Connections, on how graph technology enhances your artificial intelligence projects by providing context and connections within the underlying data.
AI Standards: Why Graphs Matter
Neo4j on how graph technology can help guide the development and application of Artificial Intelligence in ways that facilitate innovation and fair competition, public trust and confidence, while incorporating the appropriate protections.Read the Open Letter
Interview: Hilary Mason, GM of Machine Learning, Cloudera
Interview with Hilary Mason on how her team has used Neo4j, how bad ideas are the gateway to good ideas, and her view of the future of AI and machine learning.Watch Video
How Graphs Enhance Artificial Intelligence, with Neo4j's Amy Hodler
Amy Hodler, Analytics & AI Program Manager at Neo4j, speaks at GraphTour on how graph technology enhances AI, with tactical steps in how to move forward in graph data science.Watch video
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.
Digitate's ignio AI system enables organizations to optimize their most complex business areas like IT, batch manufacturing and enterprise resource planning.Read more
Learn how to leverage knowledge graphs, which are connected data graphs combined with iterative machine learning, to solve many enterprise challenges.Watch video