1 day agoRecSysOps: Best Practices for Operating a Large-Scale Recommender Systemby Ehsan Saberian and Justin Basilico video version: link Operating a large-scale recommendation system is a complex undertaking: it requires high availability and throughput, involves many services and teams, and the environment of the recommender system changes every second. For example, new members or new items may come to the…Recommendation System9 min read
Published in Netflix TechBlog·4 days agoTimestone: Netflix’s High-Throughput, Low-Latency Priority Queueing System with Built-in Support for Non-Parallelizable Workloadsby Kostas Christidis Introduction Timestone is a high-throughput, low-latency priority queueing system we built in-house to support the needs of our media encoding platform, Cosmos. Over the past 2.5 years, its usage has increased, and Timestone is now also the priority queueing engine backing our general-purpose workflow orchestration engine (Conductor), and…Distributed Systems9 min read
Sep 21Machine Learning for Fraud Detection in Streaming ServicesBy Soheil Esmaeilzadeh, Negin Salajegheh, Amir Ziai, Jeff Boote Introduction Streaming services serve content to millions of users all over the world. These services allow users to stream or download content across a broad category of devices including mobile phones, laptops, and televisions. However, some restrictions are in place, such as…Machine Learning11 min read
Sep 12New Series: Creating Media with Machine LearningBy Vi Iyengar, Keila Fong, Hossein Taghavi, Andy Yao, Kelli Griggs, Boris Chen, Cristina Segalin, Apurva Kansara, Grace Tang, Billur Engin, Amir Ziai, James Ray, Jonathan Solorzano-Hamilton Welcome to the first post in our multi-part series on how Netflix is developing and using machine learning (ML) to help creators make…Machine Learning4 min read
Published in Netflix TechBlog·Aug 15Reinforcement Learning for Budget Constrained Recommendationsby Ehtsham Elahi with James McInerney, Nathan Kallus, Dario Garcia Garcia and Justin Basilico Introduction This writeup is about using reinforcement learning to construct an optimal list of recommendations when the user has a finite time budget to make a decision from the list of recommendations. Working within the time budget…Reinforcement Learning12 min read
Published in Netflix TechBlog·Aug 10Virtual Production — A Validation Framework For Unreal EngineBy Adam Davis, Jimmy Fusil, Bhanu Srikanth and Girish Balakrishnan Game Engines in Virtual Production The use of Virtual Production and real time technologies has markedly accelerated in the past few years. At Netflix, we are always thrilled to see technology enable new ways of telling stories, and the use…Unreal Engine8 min read
Published in Netflix TechBlog·Aug 1Data Mesh — A Data Movement and Processing Platform @ NetflixBy Bo Lei, Guilherme Pires, James Shao, Kasturi Chatterjee, Sujay Jain, Vlad Sydorenko Background Realtime processing technologies (A.K.A stream processing) is one of the key factors that enable Netflix to maintain its leading position in the competition of entertaining our users. Our previous generation of streaming pipeline solution Keystone has a…7 min read
Published in Netflix TechBlog·Jul 21Formulating ‘Out of Memory Kill’ Prediction on the Netflix App as a Machine Learning Problemby Aryan Mehra with Farnaz Karimdady Sharifabad, Prasanna Vijayanathan, Chaïna Wade, Vishal Sharma and Mike Schassberger Aim and Purpose — Problem Statement The purpose of this article is to give insights into analyzing and predicting “out of memory” or OOM kills on the Netflix App. Unlike strong compute devices, TVs and set top boxes usually have…Machine Learning9 min read
Published in Netflix TechBlog·Jun 15How Netflix Content Engineering makes a federated graph searchable (Part 2)By Alex Hutter, Falguni Jhaveri, and Senthil Sayeebaba In a previous post, we described the indexing architecture of Studio Search and how we scaled the architecture by building a config-driven self-service platform that allowed teams in Content Engineering to spin up search indices easily. This post will discuss how Studio…Graph QL8 min read
Published in Netflix TechBlog·Jun 6Scaling Appsec at Netflix (Part 2)By Astha Singhal, Lakshmi Sudheer, Julia Knecht The Application Security teams at Netflix are responsible for securing the software footprint that we create to run the Netflix product, the Netflix studio, and the business. Our customers are product and engineering teams at Netflix that build these software services and platforms…Security5 min read
