By Praneeth Yenugutala, Victor Dusa, Anastasia Samoilova, Megan Ren, Justin Wei, Marcelo MaywormAt Netflix, data and machine learning practitioners run millions of workloads each day across orchestration systems and compute engines such as Titus, Spark, Trino, and Druid. On peak days, roughly 100,00...
Multi‑Ratio Video Resizing for Mobile Feed and Marketing
Serdar Cellat, Yuchen Xie, Bernadette Marcu, Blaid Burgess, Cody Jarrett, Carlos Pereira, Qing Shi, Samuel Carvajal, Manuel Pamplona, Dorothy Jung, Feiko Ritsema, Benjamin Klein, Alex Woo, Eugene Lok, Anna Pulido, Rebecka Swe, Sanford HolsappleIntroductionClips in the Netflix mobile app.At Netflix, ...
Beyond API Compatibility: Building a Tool to Measure the Blast Radius of Library Changes
By Roberto Perez Alcolea, Java Platform TeamQuasar connects a proposed API change with observed references in consumer repositories, giving the producer evidence for a release decisionAt Netflix, shared Java libraries support applications developed across a large, independently evolving repository e...
Single-tenant Titus: VM-level isolation on a multi-tenant container platform
Samartha Chandrashekar & Nick ParkerYou click play on a Friday night, and in the second that follows, dozens of services answer in milliseconds. Most of them run on Titus, our internal container platform. Titus is, in essence, a Kubernetes-based container hosting platform: we keep both the control p...
Introducing ModuleLens: Zooming into PyTorch Modules for Human and Agentic Engineering
Lida Li, Sr. Staff Software Engineer, Product ML InfraIntroductionModern recommendation systems, including those used for ads ranking and content discovery, have grown substantially in scale and complexity. This growth stems from both increasingly large model architectures. The resulting memory foot...
Ontology-Driven Observability: Building an End-to-End Knowledge Graph at Netflix Scale
Prasanna Vijayanathan, Renzo Sanchez-SilvaWhen Jake Paul and Mike Tyson stepped into the ring in November 2024, we delivered the fight to roughly 65 million concurrent streams. On an ordinary day our infrastructure takes in more than 2 billion requests and handles upwards of 38 million real-time log...
Integrating Netflix’s Foundation Model into Personalization applications
Authors: Divya Gadde, Ko-Jen Hsiao, Dhaval Patel and Moumita BhattacharyaThe motivation for building the Netflix Foundation Model for personalization was laid out in this previous blog post. The Netflix homepage is powered by several specialized models and it requires significant time and resources ...