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Scalable Annotation Service — Marken

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...

Ready-to-go sample data pipelines with Dataflow

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...

For your eyes only: improving Netflix video quality with neural networks

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...

Match Cutting at Netflix: Finding Cuts with Smooth Visual Transitions

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....

Helping VFX studios pave a path to the cloud

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...

New Series: Creating Media with Machine Learning

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...

Machine Learning for Fraud Detection in Streaming Services

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...

Seeing through hardware counters: a journey to threefold performance increase

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...

Consistent caching mechanism in Titus Gateway

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...

Orchestrating Data/ML Workflows at Scale With Netflix Maestro

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...

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