Heterogeneous Treatment Effects at Netflix
Learn more about our work on HTEs and experimentation at CODE@MIT 2025!
Nov 13, 2025 /Read More
Unlocking Entertainment Intelligence with Knowledge Graph
Author: Himanshu Singh
Nov 12, 2025 /Read More
Mount Mayhem at Netflix: Scaling Containers on Modern CPUs
Authors: Harshad Sane, Andrew Halaney
Nov 07, 2025 /Read More
Supercharging the ML and AI Development Experience at Netflix
Shashank Srikanth, Romain Cledat
Nov 04, 2025 /Read More
Post-Training Generative Recommenders with Advantage-Weighted Supervised Finetuning
Author: Keertana Chidambaram, Qiuling Xu, Ko-Jen Hsiao, Moumita Bhattacharya
Oct 24, 2025 /Read More
Behind the Streams: Real-Time Recommendations for Live Events Part 3
By: Kris Range, Ankush Gulati, Jim Isaacs, Jennifer Shin, Jeremy Kelly, Jason Tu
Oct 20, 2025 /Read More
How and Why Netflix Built a Real-Time Distributed Graph: Part 1 — Ingesting and Processing Data…
Authors: Adrian Taruc and James Dalton
Oct 17, 2025 /Read More
Data as a Product: Applying a Product Mindset to Data at Netflix
By Tomasz Magdanski
Oct 07, 2025 /Read More
100X Faster: How We Supercharged Netflix Maestro’s Workflow Engine
By Jun He, Yingyi Zhang, Ely Spears
Sep 29, 2025 /Read More
Building a Resilient Data Platform with Write-Ahead Log at Netflix
By Prudhviraj Karumanchi, Samuel Fu, Sriram Rangarajan, Vidhya Arvind, Yun Wang, John Lu
Sep 26, 2025 /Read More
