Browse past weeks of engineering reads.
Netflix needed to generate personalized homepages at scale where every row, entity ordering, and layout element is customized per user while maintaining low latency and high relevance.
How to design a notification system that intelligently decides when and how to deliver personalized notifications to users with varying urgency and cognitive load requirements.
Netflix needed to enable video editors to have more fine-grained control and predictability over AI-assisted video editing systems for creating promotional content at scale.
Building a reliable causal inference system that can accurately determine cause-and-effect relationships in data (like the impact of a show on retention) while accounting for hidden biases and confounding variables that automated agents might miss.
Netflix needed to efficiently support diverse graph query patterns (OLAP and OLTP) across different use cases with varying performance requirements using a unified abstraction layer.
Netflix needed to design a personalized notification system that efficiently decides when and how to notify users with relevant content recommendations without overwhelming them or missing critical engagement opportunities.
Netflix needed a unified abstraction layer to efficiently handle multiple graph query paradigms (OLAP and OLTP) with different performance and functionality requirements across diverse business use cases.
Netflix needed to automatically evaluate the quality and relevance of show synopses at scale to improve member discovery and engagement.
Netflix needed to design a domain-independent traffic routing system for their ML model serving infrastructure that could handle personalized experiences at scale across multiple domains while maintaining high availability.