The Problem
Pick One.
Recent AI research is generating a growing number of graph AI techniques that take advantage of graph data relationships, and the rich context it provides, however production graph data pipelines lack the performance needed to deploy these new tools at scale.
Graph AI development promises significant advances for AI application to a range of use cases thanks to the rich data context available from a graph data model. Moving graph AI techniques from the lab to production scale, however, is a significant challenge due to the limited scaling performance of graph databases.
The Solution
Quine streaming graph provides a single platform for the; 1. development of graph AI techniques, and, 2. production deployment of your algorithms on high-volume data streams. Quine even supports data ingestion and transformation of multiple data and event sources as part of the solution, allowing data scientist to define these data operations in the lab and then migrate them “as is” to production scale platforms run by operations.
Graph AI development in Quine is supports multiple ways:
- Construct your AI logic as Cypher queries and apply them via REST API.
- Apply externally built algorithms as User Defined Functions. Example
- Create custom low-level messaging primitives and node behavior on Quine. Example
- Use Quine standing queries as event-based triggers to update values on other nodes. A set of related nodes updating each other can perform the computation for, and maintain intermediate results for algorithms on a graph.
Key Value Take Away
- A single platform to define ETL operations in the lab and production
- A single platform to define, test and deploy graph AI techniques
- Build native graph AI techniques as primitives or using Quine powerful standing query capabilities
- Import externally built user defined functions for use via Quine
