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公司:Netflix

关联话题: 奈飞、网飞

Netflix(/ˈnɛtflɪks/)(官方中文译名网飞,非官方中文译名奈飞)是起源于美国、在世界各地提供网络视频点播的OTT服务公司,并同时在美国经营单一费率邮寄影像光盘出租服务,后者是使用回邮信封寄送DVD和Blu-ray出租光盘至消费者指定的收件地址。公司由里德·哈斯廷斯和马克·兰多夫在1997年8月29日成立,总部位于加利福尼亚州的洛斯盖图,1999年开始推出订阅制的服务。2009年,Netflix已可提供超过10万部电影DVD,订阅者数超过1000万人。另一方面,截至2022年6月的数据,Netflix的流服务已经在全球拥有2.20亿个订阅用户,在美国的订户已达到7330万。其主要的竞争对手有Disney+、Hulu、HBO Max、Amazon Prime Video、YouTube Premium及Apple TV+等。

Netflix在多个排行榜上均榜上有名:2017年6月6日,《2017年BrandZ最具价值全球品牌100强》公布,Netflix名列第92位。2018年10月,《财富》未来公司50强排行榜发布,Netflix排名第八。2018年12月,世界品牌实验室编制的《2018世界品牌500强》揭晓,排名第88。在《财富》2018年世界500大排名261位,并连年增长。2019年10月,位列2019福布斯全球数字经济100强榜第46名。2019年10月,Interbrand发布的全球品牌百强榜排名65。2020年1月22日,名列2020年《财富》全球最受赞赏公司榜单第16位。2022年2月,按市值计算,Netflix为全球第二大的媒体娱乐公司。2019年,Netflix加入美国电影协会(MPA)。另外,Netflix也被部分媒体列为科技巨擘之一。

Building a Media Understanding Platform for ML Innovations

The media understanding platform serves as an abstraction layer between Machine Learning (ML) algos and various applications.

NTS: Reliable Device Testing at Scale

At Netflix, we test hundreds of different device types every day, ranging from streaming sticks to smart TVs, to ensure that new version releases of the Netflix SDK continue to provide the exceptional Netflix experience that our customers expect. We also collaborate with our Partners to integrate the Netflix SDK onto their upcoming new devices, such as TVs and set top boxes. This program, known as Partner Certification, is particularly important for the business because device expansion historically has been crucial for new Netflix subscription acquisitions. The Netflix Test Studio (NTS) platform was created to support Netflix SDK testing and Partner Certification by providing a consistent automation solution for both Netflix and Partner developers to deploy and execute tests on “Netflix Ready” devices.

Over the years, both Netflix SDK testing and Partner Certification have gradually transitioned upstream towards a shift-left testing strategy. This requires the automation infrastructure to support large-scale CI, which NTS was not originally designed for. NTS 2.0 addresses this very limitation of NTS, as it has been built by taking the learnings from NTS 1.0 to re-architect the system into a platform that significantly improves reliable device testing at scale while maintaining the NTS user experience.

Data Reprocessing Pipeline in Asset Management Platform @Netflix

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, versioning, access control, sharing, triggering configured workflows like inspection, proxy generation etc. This platform has evolved from supporting studio applications to data science applications, machine-learning applications to discover the assets metadata, and build various data facts.

During this evolution, quite often we receive requests to update the existing assets metadata or add new metadata for the new features added. This pattern grows over time when we need to access and update the existing assets metadata. Hence we built the data pipeline that can be used to extract the existing assets metadata and process it specifically to each new use case. This framework allowed us to evolve and adapt the application to any unpredictable inevitable changes requested by our platform clients without any downtime. Production assets operations are performed in parallel with older data reprocessing without any service downtime. Some of the common supported data reprocessing use cases are listed below.

Elasticsearch Indexing Strategy in Asset Management Platform (AMP)

At Netflix, all of our digital media assets (images, videos, text, etc.) are stored in secure storage layers. We built an asset management platform (AMP), codenamed Amsterdam, in order to easily organize and manage the metadata, schema, relations and permissions of these assets. It is also responsible for asset discovery, validation, sharing, and for triggering workflows.

Amsterdam service utilizes various solutions such as Cassandra, Kafka, Zookeeper, EvCache etc. In this blog, we will be focusing on how we utilize Elasticsearch for indexing and search the assets.

Data ingestion pipeline with Operation Management

At Netflix, to promote and recommend the content to users in the best possible way there are many Media Algorithm teams which work hand in hand with content creators and editors. Several of these algorithms aim to improve different manual workflows so that we show the personalized promotional image, trailer or the show to the user.

These media focused machine learning algorithms as well as other teams generate a lot of data from the media files, which we described in our previous blog, are stored as annotations in Marken. We designed a unique concept called Annotation Operations which allows teams to create data pipelines and easily write annotations without worrying about access patterns of their data from different applications.

Scaling Media Machine Learning at Netflix

We tackle some of the unique challenges of scaling multimodal machine learning models that operate on media assets (video, audio, and text).

Discovering Creative Insights in Promotional Artwork

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 source of information for the member to make a decision to watch, or not watch, a title. The stories on Netflix are multidimensional and there are many ways that a single story could appeal to different members. We want to show members the images, trailers, and synopses that are most helpful to them for making a watch decision.

Scalable Annotation Service — Marken

In Marken (Scalable Annotation Service at Netflix), an annotation is a piece of metadata which can be attached to an object from any domain.

Ready-to-go sample data pipelines with Dataflow

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 was a deep dive into one of the more technical aspects of Dataflow and didn’t properly introduce this tool in the first place. This time we’ll try to give justice to the intro and then we will focus on one of the very first features Dataflow came with. That feature is called sample workflows, but before we start in let’s have a quick look at Dataflow in general.

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

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 technologies. For example, we invest in next-generation, royalty-free codecs and sophisticated video encoding optimizations. Recently, we added another powerful tool to our arsenal: neural networks for video downscaling. In this tech blog, we describe how we improved Netflix video quality with neural networks, the challenges we faced and what lies ahead.

Match Cutting at Netflix: Finding Cuts with Smooth Visual Transitions

In film, a match cut is a transition between two shots that uses similar visual framing, composition, or action to fluidly bring the viewer from one scene to the next. It is a powerful visual storytelling tool used to create a connection between two scenes.

Embracing the Differences : Inside the Netflix API Redesign

The key driver for this redesigned API is the fact that there are a range of differences across the 800+ device types that we support. Most APIs (including the REST API that Netflix has been using since 2008) treat these devices the same, in a generic way, to make the server-side implementations more efficient. And there is good reason for this approach. Providing an OSFA API allows the API team to maintain a solid contract with a wide range of API consumers because the API team is setting the rules for everyone to follow.

While effective, the problem with the OSFA approach is that its emphasis is to make it convenient for the API provider, not the API consumer. Accordingly, OSFA is ignoring the differences of these devices; the differences that allow us to more optimally take advantage of the rich features offered on each.

Seeing through hardware counters: a journey to threefold performance increase

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 domain. There is, however, a class of problems that requires an even stronger level of magnification going deeper down the stack to introspect CPU microarchitecture. In this blogpost we describe one such problem and the tools we used to solve it.

Consistent caching mechanism in Titus Gateway

Titus 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 users increased over the years, the load and pressure on the system increased substantially. The original assumptions and architectural choices were no longer viable. This blog post presents how our current iteration of Titus deals with high API call volumes by scaling out horizontally.

We introduce a caching mechanism in the API gateway layer, allowing us to offload processing from singleton leader elected controllers without giving up strict data consistency and guarantees clients observe. Titus API clients always see the latest (not stale) version of the data regardless of which gateway node serves their request, and in which order.

Orchestrating Data/ML Workflows at Scale With Netflix Maestro

At 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 to serve various business needs. These include ETL pipelines, ML model training workflows, batch jobs, etc. As Big data and ML became more prevalent and impactful, the scalability, reliability, and usability of the orchestrating ecosystem have increasingly become more important for our data scientists and the company.

In this blog post, we introduce and share learnings on Maestro, a workflow orchestrator that can schedule and manage workflows at a massive scale.

How Product Teams Can Build Empathy Through Experimentation

A conversation between Travis Brooks, Netflix Product Manager for Experimentation Platform, and George Khachatryan, OfferFit CEO.

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