Join us for a free virtual event on data engineering and systems architecture for machine learning recommender systems
apply() is an event series for machine learning and data teams to discuss the practical data engineering challenges faced when building operational machine learning systems. Participants learn from industry experts and share best practices with the community.
apply(recsys) focuses on the specific challenges of building recommender systems. Join us to discuss best practice development patterns, tools of choice, and emerging architectures to successfully build and manage production RecSys applications.
Topics include:
- Best practices development patterns
- Systems architecture
- Tooling and infrastructure of choice
- Building and managing real-time data pipelines
- Serving predictions at scale
Crawl, walk, run: a practical introduction to applied recommender systems
Recommend API: Slack’s Unified End-to-End Machine Learning Infrastructure to Generate Recommendations
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.
Swag Giveaway
Intermission between the incredible talks. While we hang out and rest our minds Demetrios will be cracking jokes and giving out some Swaaaaag!
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.
Bad Musical Freestyles
You suggest the lyrics and Demetrios will sing about whatever you desire!
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.
Networking
Meet others who are at the event!
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.
