Piyush Narang

Modern machine learning applications demand features computed in near real-time while maintaining low-latency serving — a challenge that becomes exponentially harder at scale. This talk explores Chronon, an open-source feature platform battle-tested in production at Stripe, Airbnb, Netflix, and OpenAI, and how it bridges the gap between streaming data infrastructure and production ML systems. Traditional feature engineering pipelines force teams to choose between freshness and latency, leading to complex dual pipeline architectures that are expensive to maintain and prone to training-serving skew. Chronon solves this by providing a unified abstraction over batch and streaming computation, enabling teams to define features once and serve them with sub-100ms latencies while keeping them updated in near real-time. We'll demonstrate how Chronon can be used in a wide variety of ML applications such as real-time fraud prevention as well as more complex use-cases that require chaining feature computation with model inference / embedding pipelines such as two-tower search recommendation systems. Additionally, we'll explore how Chronon minimizes computation in the serving hot-path for…

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