Session

Scaling Real-Time Fraud Detection With Databricks: Lessons From DraftKings

Overview

ExperienceIn Person
TypeBreakout
TrackArtificial Intelligence
IndustryMedia and Entertainment
TechnologiesApache Spark, Delta Lake, MLFlow
Skill LevelIntermediate
Duration40 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