Published in Netflix TechBlog·PinnedMatch Cutting: Finding Cuts with Smooth Visual Transitions Using Machine LearningBy 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 Blank Creating Media with Machine Learning episode 1 Introduction At Netflix, part of what we do is build tools to help our creatives…Machine Learning12 min readMachine Learning12 min read
Published in Netflix TechBlog·1 day agoDiscovering Creative Insights in Promotional ArtworkBy Grace Tang, Aneesh Vartakavi, Julija Bagdonaite, Cristina Segalin, and Vi Iyengar When 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 a critical…Data Science9 min readData Science9 min read
Published in Netflix TechBlog·6 days agoScalable Annotation Service — Markenby Varun Sekhri, Meenakshi Jindal — Introduction At 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 services at a later point want to annotate their objects or…Annotations11 min readAnnotations11 min read
Jan 11Causal Machine Learning for Creative InsightsA framework to identify the causal impact of successful visual components. By Billur Engin, Yinghong Lan, Grace Tang, Cristina Segalin, Kelli Griggs, Vi Iyengar Introduction At Netflix, we want our viewers to easily find TV shows and movies that resonate and engage. Our creative team helps make this happen by…Causal Inference13 min readCausal Inference13 min read
Dec 17, 2022Data Reprocessing Pipeline in Asset Management Platform @NetflixBy Meenakshi Jindal Overview At 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 asset cycle of schema validation…Data Processing7 min readData Processing7 min read
Published in Netflix TechBlog·Dec 4, 2022Ready-to-go sample data pipelines with Dataflowby Jasmine Omeke, Obi-Ike Nwoke, Olek Gorajek Intro This 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 management with Dataflow. That article…Data Engineering13 min readData Engineering13 min read
Published in Netflix TechBlog·Nov 15, 2022Helping VFX studios pave a path to the cloudBy: Peter Cioni (Netflix), Alex Schworer (Netflix), Mac Moore (Conductor Tech.), Rachel Kelley (AWS), Ranjit Raju (AWS) Rendering is core to the VFX process VFX studios around the world create amazing imagery for Netflix productions. Nearly every show that is produced today includes digital visual effects, from the creatures in Stranger Things, to recreating historic London in…Vfx5 min readVfx5 min read
Published in Netflix TechBlog·Nov 14, 2022For your eyes only: improving Netflix video quality with neural networksby Christos G. Bampis, Li-Heng Chen and Zhi Li When 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…Neural Networks6 min readNeural Networks6 min read
Nov 10, 2022Netflix at MIT CODE 2022Netflix 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…Ab Testing3 min readAb Testing3 min read
Published in Netflix TechBlog·Nov 9, 2022Seeing through hardware counters: a journey to threefold performance increaseBy Vadim Filanovsky and Harshad Sane In 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 insight within each…Performance10 min readPerformance10 min read
