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Events

5:30 PM - 9:00 PM GMT
Tuesday, Dec 6
5:30 PM - 9:00 PM GMT
apply(recsys)
Join us for a free virtual event on data engineering and systems architecture for machine learning recommender systemsapply() is an event series for machine learning and data teams to discuss the practical data engineering challenges faced when building o
Mike Del Balso
Katrina Ni
Youlong Cheng
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Content

apply(recsys) Conference 2022 | Workshop: Choosing Feast or Tecton for Your RecSys Architecture
video
Feature stores can be essential components of your infrastructure for recommender systems. They simplify the process of deploying and managing RecSys models. But users often wonder which one is better suited for their use cases - Feast or Tecton? These two products are very different and address different requirements. In this workshop, we’ll give a hands-on, step-by-step example of building RecSys models using both Feast and Tecton. And we’ll provide our recommendations on when to use which product.
Dec 12th, 2022 | Views 265
apply(recsys) Conference 2022 | Lessons Learned: The Journey to Operationalizing Recommender Systems
video
Join us in this panel discussion to hear from ML practitioners on their journey to implementing Recommender Systems. We’ll discuss the most common challenges encountered when getting started, and best practices to address them. We’ll explore organizational dynamics, recommended tools, and how to align business requirements with technical capabilities. You’ll hear about approaches to phasing in Recommender Systems, starting small and progressively iterating on more sophisticated solutions.
Dec 12th, 2022 | Views 310
apply(recsys) Conference 2022 | Monolith: Real-Time Recommendation System With Collisionless Embedding Table
video
We’ll provide an introduction to Monolith, a system tailored for online training. Our design has been driven by observations of our application workloads and production environment that reflects a marked departure from other recommendations systems. Our contributions are manifold: first, we crafted a collisionless embedding table with optimizations such as expirable embeddings and frequency filtering to reduce its memory footprint; second, we provide an production-ready online training architecture with high fault-tolerance; finally, we proved that system reliability could be traded-off for real-time learning. Monolith has successfully landed in the BytePlus Recommend product.
Dec 12th, 2022 | Views 229