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apply(recsys) 2022
# apply(recsys) 2022
# Feature Stores
# Tecton

apply(recsys) Conference 2022 | Workshop: Choosing Feast or Tecton for Your RecSys Architecture

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.
Jake Noble
Danny Chiao
Jake Noble & Danny Chiao · Dec 12th, 2022
apply(recsys) Conference 2022 | Workshop: Choosing Feast or Tecton for Your RecSys Architecture
Popular topics
# Systems and Architecture
# Production Use Case
# Data engineering
# Open Source
# Feature Stores
# Model serving
# Organization and Processes
# Explainability and Observability
# Model training
# Research
All (4)
Video (4)
Most Recent
Mike Del Balso
Jacopo Tagliabue
Marc Lindner
Agnes van Belle
Krystal Zeng
Mike Del Balso, Jacopo Tagliabue, Marc Lindner, Agnes van Belle & Krystal Zeng · Dec 12th, 2022

apply(recsys) Conference 2022 | Lessons Learned: The Journey to Operationalizing Recommender Systems

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.
# apply(recsys) 2022
# Panel Discussion
# Production Use Case
apply(recsys) Conference 2022 | Lessons Learned: The Journey to Operationalizing Recommender Systems
Youlong Cheng
Youlong Cheng · Dec 12th, 2022

apply(recsys) Conference 2022 | Monolith: Real-Time Recommendation System With Collisionless Embedding Table

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.
# apply(recsys) 2022
# Production Use Case
# Model training
apply(recsys) Conference 2022 | Monolith: Real-Time Recommendation System With Collisionless Embedding Table
Slack, as a product, presents many opportunities for recommendation, where we can make suggestions to simplify the user experience and make it more delightful. Each one seems like a terrific use case for machine learning, but it isn’t realistic for us to create a bespoke solution for each. In the talk, we’ll dive into the Recommend API, a unified framework the team built over the years that allows us to quickly bootstrap new recommendation use cases. Behind the scenes, these recommenders reuse a common set of infrastructure for every part of the recommendation engine, such as data processing, model training, candidate generation, and monitoring. This has allowed us to deliver a number of different recommendation models across the product, driving improved customer experience in a variety of contexts.
# apply(recsys) 2022
# Production Use Case
apply(recsys) Conference 2022 | Recommend API: Slack’s Unified End-to-End ML Infrastructure
# apply(recsys) 2022
# Systems and Architecture
# Production Use Case
apply(recsys) Conference 2022 | Crawl, walk, run: a practical intro to applied recommender systems
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