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Rebuilding Netflix Video Processing Pipeline with Microservices

The Netflix TechBlog

The Netflix video processing pipeline went live with the launch of our streaming service in 2007. To that end, the Video and Image Encoding team in Encoding Technologies (ET) has spent the last few years rebuilding the video processing pipeline on our next-generation microservice-based computing platform Cosmos.

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Designing Instagram

High Scalability

Design a photo-sharing platform similar to Instagram where users can upload their photos and share it with their followers. Generating machine learning based personalized recommendations to discover new people, photos, videos, and stories relevant one’s interest. High Level Design. Component Design. API Design.

Design 334
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Netflix Video Quality at Scale with Cosmos Microservices

The Netflix TechBlog

Moorthy and Zhi Li Introduction Measuring video quality at scale is an essential component of the Netflix streaming pipeline. Perceptual quality measurements are used to drive video encoding optimizations , perform video codec comparisons , carry out A/B testing and optimize streaming QoE decisions to mention a few.

Media 171
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Building Netflix’s Distributed Tracing Infrastructure

The Netflix TechBlog

Investigating a video streaming failure consists of inspecting all aspects of a member account. Now let’s look at how we designed the tracing infrastructure that powers Edgar. This insight led us to build Edgar: a distributed tracing infrastructure and user experience.

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Scaling Media Machine Learning at Netflix

The Netflix TechBlog

Our goal in building a media-focused ML infrastructure is to reduce the time from ideation to productization for our media ML practitioners. In addition, we provide a unified library that enables ML practitioners to seamlessly access video, audio, image, and various text-based assets.

Media 290
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Evolution of ML Fact Store

The Netflix TechBlog

We will share how its design has evolved over the years and the lessons learned while building it. To understand Axion’s design, we need to know the various components that interact with it. Figure 1: Netflix ML Architecture Fact: A fact is data about our members or videos. Time is a critical component of Axion?

Storage 187
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Growth Engineering at Netflix- Creating a Scalable Offers Platform

The Netflix TechBlog

by Eric Eiswerth Background Netflix has been offering streaming video-on-demand (SVOD) for over 10 years. In particular, it’s our job to design and build the systems and protocols that enable customers from all over the world to sign up for Netflix with the plan features and incentives that best suit their needs.