Scaling Real-Time Fraud Detection With Databricks: Lessons From DraftKings
Overview
Experience | In Person |
---|---|
Type | Breakout |
Track | Artificial Intelligence |
Industry | Media and Entertainment |
Technologies | Apache Spark, Delta Lake, MLFlow |
Skill Level | Intermediate |
Duration | 40 min |
At DraftKings, ensuring secure, fair gaming requires detecting fraud in real time with both speed and precision. In this talk, we’ll share how Databricks powers our fraud detection pipeline, integrating real-time streaming, machine learning and rule-based detection within a PySpark framework. Our system enables rapid model training, real-time inference and seamless feature transformation across historical and live data. We use shadow mode to test models and rules in live environments before deployment. Collaborating with Databricks, we push online feature store performance and enhance real-time PySpark capabilities. We'll cover PySpark-based feature transformations, real-time inference, scaling challenges and our migration from a homegrown system to Databricks. This session is for data engineers and ML practitioners optimizing real-time AI workloads, featuring a deep dive, code snippets and lessons from building and scaling fraud detection.
Session Speakers
IMAGE COMING SOON
Monika Hristova
/PSE
Draftkings
Greg Von Pless
/Principal Data Science Engineer
Draftkings