PinnedHeterogeneous Treatment Effects at NetflixLearn more about our work on HTEs and experimentation at CODE@MIT 2025!2d ago2d ago
PinnedUnlocking Entertainment Intelligence with Knowledge GraphAuthor: Himanshu Singh2d agoA response icon32d agoA response icon3
PinnedPublished inNetflix TechBlogSupercharging the ML and AI Development Experience at NetflixNetflix accelerates ML/AI development with Metaflow’s new spin command, enabling notebook-like iteration in production-ready workflows.Nov 4A response icon4Nov 4A response icon4
PinnedPublished inNetflix TechBlogPost-Training Generative Recommenders with Advantage-Weighted Supervised FinetuningAuthor: Keertana Chidambaram, Qiuling Xu, Ko-Jen Hsiao, Moumita BhattacharyaOct 24A response icon4Oct 24A response icon4
PinnedPublished inNetflix TechBlogBehind the Streams: Real-Time Recommendations for Live Events Part 3By: Kris Range, Ankush Gulati, Jim Isaacs, Jennifer Shin, Jeremy Kelly, Jason TuOct 20A response icon4Oct 20A response icon4
How and Why Netflix Built a Real-Time Distributed Graph: Part 2 — Building a Scalable Storage LayerAuthors: Luis Medina and Ajit Koti5h ago5h ago
Mount Mayhem at Netflix: Scaling Containers on Modern CPUsAuthors: Harshad Sane, Andrew HalaneyNov 7A response icon2Nov 7A response icon2
Published inNetflix TechBlogHow and Why Netflix Built a Real-Time Distributed Graph: Part 1 — Ingesting and Processing Data…Authors: Adrian Taruc and James DaltonOct 17A response icon19Oct 17A response icon19
Data as a Product: Applying a Product Mindset to Data at NetflixIntroduction: What if we treated data with the same care and intentionality as a consumer-facing product? Adopting a “data as a product”…Oct 7A response icon17Oct 7A response icon17
Published inNetflix TechBlog100X Faster: How We Supercharged Netflix Maestro’s Workflow EngineBy Jun He, Yingyi Zhang, Ely SpearsSep 29A response icon4Sep 29A response icon4
