Netflix

Optimizing Recommendation Systems with JDK’s Vector API

Netflix's Ranker service had a video serendipity scoring feature (computing how different a title is from a user's watch history) consuming ~7.5% of total CPU per node, creating a significant performance bottleneck at their enormous scale.

ml-systems real-time-systems
5 min
Netflix

MediaFM: The Multimodal AI Foundation for Media Understanding at Netflix

Netflix needed scalable, deep machine-level understanding of every piece of content across an expanding catalog (including live events and podcasts) to power recommendations and discovery, but building separate models per content type and modality doesn't scale.

ml-systems microservices
5 min
Netflix

Mount Mayhem at Netflix: Scaling Containers on Modern CPUs

Netflix needed to spin up hundreds of containers in seconds to serve streaming traffic, but after modernizing their container runtime, they hit an unexpected performance bottleneck rooted in CPU architecture that impaired container scaling efficiency.

distributed-systems real-time-systems
5 min
Netflix

How Temporal Powers Reliable Cloud Operations at Netflix

Netflix needed reliable orchestration for business-critical cloud operations across teams like Open Connect CDN and Live reliability, but faced operational challenges as Temporal adoption grew since 2021.

distributed-systems microservices
5 min