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 services at a later point want to annotate their objects...
3d
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 management with Dataflow. That article was a deep dive into...
Dec 2022
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 technologies. For example, we invest in next-generation, royalty-free...
Nov 2022
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 creatives make exciting videos to share with the world....
Nov 2022
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 effects, from the creatures in Stranger Things, to recreating...
Nov 2022
By 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-HamiltonWelcome to the first post in our multi-part series on how Netflix is developing and using machine learning (ML) to help creators make better media — from TV shows...
Nov 2022
By Soheil Esmaeilzadeh, Negin Salajegheh, Amir Ziai, Jeff BooteIntroductionStreaming 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 the number...
Nov 2022
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 insight within each domain. There is, however, a class...
Nov 2022
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-increasing volume of containers. As the number of Titus...
Nov 2022
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 transformations. A large number of batch workflows run daily...
Oct 2022
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