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Pre-Registration is REQUIRED. RSVP here - https://luma.com/rayhu23h

## ODSC AI & Chalk Present: Relevance at Decision Time

Most marketplace recommender systems don’t fail because of poor model quality—they fail because of stale data.

When systems rely on rigid batch pipelines and precomputed features, the information is often outdated by the time a ranking decision is actually made. Join us in San Francisco for an evening dedicated to solving the production bottlenecks of real-time machine learning.

The Talk
Building Recommender Systems with On-Demand Feature Computation
Elliot Marx (Co-founder of Chalk) will break down why production recommenders break and introduce a better way: computing features on demand at decision time. Using a query execution engine purpose-built for real-time ML, he’ll demonstrate how to maintain relevance under strict latency and scale constraints.

The Speaker
Elliot Marx, Co-founder of Chalk has spent the last decade building ML systems that operate under intense real-world constraints. Before founding Chalk, he built the data engineering platform for fraud at Affirm and later sold his previous company to Credit Karma, where he led ML infrastructure development.

Agenda & Logistics
📅 Date: 29th January 2026 Time: 6:00 PM – 8:00 PM PT 📍 Location: 55 Stockton St, Floor 4, San Francisco, CA 94108 🍕 Food: Pizza and refreshments included!

  • 6:00 PM – 6:30 PM: Networking & Bites/Drinks
  • 6:30 PM – 7:15 PM: Presentation + Live-Code Demo
  • 7:15 PM – 7:30 PM: Q&A
  • 7:30 PM – 8:00 PM: Networking

Space is limited, so please RSVP to secure your spot!​

## Useful Links

Events in San Francisco, CA
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AI summary

By Meetup

In-person meetup for ML engineers/data scientists on real-time recommender systems; learn to compute features on demand at decision time to meet latency constraints.

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