Session Description

Traditional feature engineering pipelines force teams to choose between freshness and latency, leading to complex dual architectures that are expensive to maintain and prone to training-serving skew. For search and recommendation systems, this trade-off is particularly painful: you need a blend of fresh signals (user, query, and item features) and their corresponding embeddings for retrieval and ranking, but can’t sacrifice the sub-100ms latencies these systems need to meet.

This talk explores how Chronon solves this challenge through a unified abstraction over batch and streaming computation, allowing teams to define features once and serve them with minimal latency while keeping them updated in near real-time. Chronon has been battle-tested in production at companies like Stripe, Airbnb, Netflix, and OpenAI, serving billions of predictions daily.

We’ll use a two-tower search retrieval and ranking pipeline as our primary case study, walking through:

• Computing real-time user and item embeddings for candidate retrieval
• Chaining embedding computation with tabular features to power ranking models
• Minimizing computation in the serving hot-path reducing infrastructure costs by orders of magnitude

Audience takeways:

• How Chronon unifies batch and streaming feature computation
• Chronon’s pluggable architecture with respect to table formats, streaming buses, KV stores and model platforms
• Chronon’s approach to minimize serving latency while maximizing feature freshness in production ML systems
• How one can build ML pipelines that chain feature computation with model inference / embedding
• Real-world lessons from companies serving billions of predictions daily

This talk sits at the intersection of search, data streaming, and AI in production—ideal for ML engineers, search platform teams, and anyone building real-time intelligent applications at scale.