Mingyang Ge

Ever wondered how industry leaders handle thousands of ML predictions per second? This session reveals the architecture behind high-performance model serving systems on Databricks. We'll explore how to build inference pipelines that efficiently scale to handle massive request volumes while maintaining low latency. You'll learn how to leverage Feature Store for consistent, low-latency feature lookups and implement auto-scaling strategies that optimize both performance and cost. Key takeaways: - Determining optimal compute capacity using the QPS × model execution time formula - Configuring Feature Store for high-throughput, low-latency feature retrieval - Managing cold starts and scaling strategies for latency-sensitive applications - Implementing monitoring systems that provide visibility into inference performance Whether you're serving recommender systems or real-time fraud detection models, you'll gain practical strategies for building enterprise-grade ML serving systems.