Real-Time Feature Aggregation at Scale: iFood’s Path to Sub-Second Latency

At iFood, real-time ML features are essential for delivering personalized and responsive user experiences across critical use cases such as fraud detection, recommendations, and promotions. In this talk, we’ll walk through how we built a low-latency feature platform that aggregates and serves features in under one second using Spark Structured Streaming and Redis. The platform enables real-time updates that power models reacting instantly to user behavior, supporting high-throughput, low-latency pipelines in a production environment.