Join us for a live demo and build your graph data science notebook via Machine Learning Workbench in less than 60 minutes.
Identifying fraudulent behaviors is becoming increasingly complex as technology advances and fraudsters constantly evolve new ways to exploit people, companies, and institutions. The complexity grows as companies introduce new channels, platforms, and devices for customers to engage with their brands, manage their accounts, and make transactions. In this webinar, we will cover how businesses utilize TigerGraph to extract features that result in a more performant machine learning model compared to those using traditional feature sets.
Attend this webinar to learn:
- Getting started with TigerGraph's Machine Learning Workbench
- How to build a fraud detection graph database solution
- Hand-on python notebook walkthrough
- Exploring Graph Data Science algorithms
- Executing Graph + Machine Learning techniques
Speaker - Parker Erickson, Machine Learning Engineer