Building Production-Ready Recommender Systems with Feast


Recommender systems are highly prevalent in modern applications and services but are notoriously difficult to build and maintain. Organizations face challenges such as complex data dependencies, data leakage, and frequently changing data/models. These challenges are compounded when building, deploying, and maintaining ML pipelines spans data scientists and engineers. Feature stores help address many of the operational challenges associated with recommender systems.

In this talk, we explore:
• Challenges of building recommender systems
• Strategies for reducing latency, while balancing requirements for freshness
• Challenges in mitigating data quality issues
• Technical and organizational challenges feature stores solve
• How to integrate Feast, an open-source feature store, into an existing recommender system to support production systems


Danny Chiao is an engineering lead at Tecton/Feast Inc working on building a next-generation feature store. Previously, Danny was a technical lead at Google working on end to end machine learning problems within Google Workspace, helping build privacy-aware ML platforms / data pipelines and working with research and product teams to deliver large-scale ML powered enterprise functionality. Danny holds a Bachelor’s degree in Computer Science from MIT.

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