Graph Gurus Episode 21: Real-Time Fraud Detection at Scale - Integrating Real-Time Deep-Link Graph Analytics with Spark AI

Recorded October 30, 2019 

As data grows in size and connectedness dramatically, the potential for graph-enriched machine learning grows likewise, but scalable technologies are needed to both build models and apply them in real-time. Real-time deep-link graph pattern matching and analytics provides new opportunities for enriching your machine learning models with graph features. In addition to the real-time deep-link aspect, the ability to process large datasets in a production pipeline provides a synergistic approach for the two distributed and performant platforms: Spark and TigerGraph. The TigerGraph graph database provides scalable real-time deep link graph analytics and augments Spark with graph analytics and predictions for a wide range of Machine Learning use cases. 

In this Graph Gurus episode, we:

  • Explain the architecture and technical implementation for a TigerGraph + Spark graph-enhanced Machine Learning pipeline
  • Use TigerGraph both before training to extract (graph and non-graph) features and after training to apply the model on streaming data
  • Use Spark to train and tune machine learning models at scale
  • Present a solution in production at China Mobile that detects and prevents phone-based scams using machine learning with TigerGraph
  • Demo the data flow between Spark and TigerGraph via TigerGraph’s JDBC driver  
  • Emma Liu, Senior Product Manager
    • Emma originally gave this talk at the Spark + AI Summit in Amsterdam