Frequent verification codes may appear for non-Mainland China IP visits. If visitors are severely affected, please send [IP|ASN](|AS14618) to the bottom mailbox to add to the whitelist


2023-01-31 00:16   Discovering Creative Insights in Promotional Artwork    #[email protected] #安全文章 By Grace Tang, Aneesh Vartakavi, Julija Bagdonaite, Cristina Segalin, and Vi IyengarWhen members are shown a title on Netflix, the displayed artwork, trailers, and synopses are personalized. That means members see the assets that are most likely to help them make an informed choice. These assets are

2023-01-26 11:07   Scalable Annotation Service — Marken    #[email protected] #安全文章 Scalable Annotation Service — Markenby Varun Sekhri, Meenakshi JindalIntroductionAt Netflix, we have hundreds of micro services each with its own data models or entities. For example, we have a service that stores a movie entity’s metadata or a service that stores metadata about images. All of these

2023-01-11 23:01   Causal Machine Learning for Creative Insights    #[email protected] #安全文章 A framework to identify the causal impact of successful visual components.By Billur Engin, Yinghong Lan, Grace Tang, Cristina Segalin, Kelli Griggs, Vi IyengarIntroductionAt Netflix, we want our viewers to easily find TV shows and movies that resonate and engage. Our creative team helps make this ha

2022-12-17 09:34   Data Reprocessing Pipeline in Asset Management Platform @Netflix    #[email protected] #安全文章 By Meenakshi JindalOverviewAt Netflix, we built the asset management platform (AMP) as a centralized service to organize, store and discover the digital media assets created during the movie production. Studio applications use this service to store their media assets, which then goes through an asse

2022-12-04 08:10   Ready-to-go sample data pipelines with Dataflow    #[email protected] #安全文章 by Jasmine Omeke, Obi-Ike Nwoke, Olek GorajekIntroThis post is for all data practitioners, who are interested in learning about bootstrapping, standardization and automation of batch data pipelines at Netflix.You may remember Dataflow from the post we wrote last year titled Data pipeline asset manag

2022-11-17 23:01   Match Cutting at Netflix: Finding Cuts with Smooth Visual Transitions    #[email protected] #安全文章 By Boris Chen, Kelli Griggs, Amir Ziai, Yuchen Xie, Becky Tucker, Vi Iyengar, Ritwik Kumar, Keila Fong, Nagendra Kamath, Elliot Chow, Robert Mayer, Eugene Lok, Aly Parmelee, Sarah BlankCreating Media with Machine Learning episode 1IntroductionAt Netflix, part of what we do is build tools to help our

2022-11-16 01:01   Helping VFX studios pave a path to the cloud    #[email protected] #安全文章 By: Peter Cioni (Netflix), Alex Schworer (Netflix), Mac Moore (Conductor Tech.), Rachel Kelley (AWS), Ranjit Raju (AWS)Rendering is core to the VFX processVFX studios around the world create amazing imagery for Netflix productions. Nearly every show that is produced today includes digital visual eff

2022-11-15 00:30   For your eyes only: improving Netflix video quality with neural networks    #[email protected] #安全文章 by Christos G. Bampis, Li-Heng Chen and Zhi LiWhen you are binge-watching the latest season of Stranger Things or Ozark, we strive to deliver the best possible video quality to your eyes. To do so, we continuously push the boundaries of streaming video quality and leverage the best video technologie

2022-11-11 00:02   Netflix at MIT CODE 2022    #[email protected] #安全文章 Netflix was proud to be the primary sponsor of the 2022 Conference on Digital Experimentation (CODE), hosted by the MIT Initiative on the Digital Economy. Our cast of Netflixers were excited to see new and old faces in person after two long years! In addition to stepping up our sponsorship game, we

2022-11-10 07:47   Seeing through hardware counters: a journey to threefold performance increase    #[email protected] #安全文章 By Vadim Filanovsky and Harshad SaneIn one of our previous blogposts, A Microscope on Microservices we outlined three broad domains of observability (or “levels of magnification,” as we referred to them) — Fleet-wide, Microservice and Instance. We described the tools and techniques we use to gain in

2022-11-04 03:19   Consistent caching mechanism in Titus Gateway    #[email protected] #安全文章 by Tomasz Bak and Fabio KungIntroductionTitus is the Netflix cloud container runtime that runs and manages containers at scale. In the time since it was first presented as an advanced Mesos framework, Titus has transparently evolved from being built on top of Mesos to Kubernetes, handling an ever-in

2022-10-19 04:43   Orchestrating Data/ML Workflows at Scale With Netflix Maestro    #[email protected] #安全文章 by Jun He, Akash Dwivedi, Natallia Dzenisenka, Snehal Chennuru, Praneeth Yenugutala, Pawan DixitAt Netflix, Data and Machine Learning (ML) pipelines are widely used and have become central for the business, representing diverse use cases that go beyond recommendations, predictions and data transform

2022-10-13 04:56   How Product Teams Can Build Empathy Through Experimentation    #[email protected] #安全文章 A conversation between Travis Brooks, Netflix Product Manager for Experimentation Platform, and George Khachatryan, OfferFit CEONote: I’ve known George for a little while now, and as we’ve talked a lot about the philosophy of experimentation, he kindly invited me to their office (virtually) for thei

2022-10-01 06:43   RecSysOps: Best Practices for Operating a Large-Scale Recommender System    #[email protected] #安全文章 by Ehsan Saberian and Justin Basilicovideo version: linkOperating 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

2022-09-28 23:40   Timestone: Netflix’s High-Throughput, Low-Latency Priority Queueing System with Built-in Support…    #[email protected] #安全文章 Timestone: Netflix’s High-Throughput, Low-Latency Priority Queueing System with Built-in Support for Non-Parallelizable Workloadsby Kostas ChristidisIntroductionTimestone is a high-throughput, low-latency priority queueing system we built in-house to support the needs of our media encoding platform,