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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。

How Netflix Content Engineering makes a federated graph searchable (Part 2)

In a previous post, we described the indexing architecture of Studio Search and how we scaled the architecture by building a config-driven self-service platform that allowed teams in Content Engineering to spin up search indices easily.

This post will discuss how Studio Search supports querying the data available in these indices.

Scaling Appsec at Netflix (Part 2)

The Application Security teams at Netflix are responsible for securing the software footprint that we create to run the Netflix product, the Netflix studio, and the business. Our customers are product and engineering teams at Netflix that build these software services and platforms. The Netflix cultural values of ‘Context not Control’ and ‘Freedom and Responsibility’ strongly influence how we do Security at Netflix. Our goal is to manage security risks to Netflix via clear, opinionated security guidance, and by providing risk context to Netflix engineering teams to make pragmatic risk decisions at scale.

A Survey of Causal Inference Applications at Netflix

At Netflix, we want to entertain the world through creating engaging content and helping members discover the titles they will love. Key to that is understanding causal effects that connect changes we make in the product to indicators of member joy.

Evolution of ML Fact Store

At Netflix, we aim to provide recommendations that match our members’ interests. To achieve this, we rely on Machine Learning (ML) algorithms. ML algorithms can be only as good as the data that we provide to it. This post will focus on the large volume of high-quality data stored in Axion — our fact store that is leveraged to compute ML features offline. We built Axion primarily to remove any training-serving skew and make offline experimentation faster. We will share how its design has evolved over the years and the lessons learned while building it.

How Netflix Content Engineering makes a federated graph searchable

Over the past few years Content Engineering at Netflix has been transitioning many of its services to use a federated GraphQL platform. GraphQL federation enables domain teams to independently build and operate their own Domain Graph Services (DGS) and, at the same time, connect their domain with other domains in a unified GraphQL schema exposed by a federated gateway.

Rapid Event Notification System at Netflix

This blog post gives an overview of the Rapid Event Notification System at Netflix and shares some of the learnings gained while building it.

Data pipeline asset management with Dataflow

The problem of managing scheduled workflows and their assets is as old as the use of cron daemon in early Unix operating systems. The design of a cron job is simple, you take some system command, you pick the schedule to run it on and you are done.

Fixing Performance Regressions Before they Happen

At Netflix we’re proud of our reliability and we want to keep it that way. To that end, it’s important that we prevent significant performance regressions from reaching the production app. Sluggish scrolling or late rendering is frustrating and triggers accidental navigations. Choppy playback makes watching a show less enjoyable. Any performance regression that makes it into a product release will degrade user experience, so the challenge is to detect and fix such regressions before they ship.

This post describes how the Netflix TVUI team implemented a robust strategy to quickly and easily detect performance anomalies before they are released — and often before they are even committed to the codebase.

Experimentation is a major focus of Data Science across Netflix

Earlier posts in this series covered the basics of A/B tests (Part 1 and Part 2 ), core statistical concepts (Part 3 and Part 4), and how to build confidence in decisions based on A/B test results (Part 5). Here we describe the role of Experimentation and A/B testing within the larger Data Science and Engineering organization at Netflix, including how our platform investments support running tests at scale while enabling innovation. The subsequent and final post in this series will discuss the importance of the culture of experimentation within Netflix.

Snaring the Bad Folks

Cloud security is a hard problem, but an even harder one is cloud security at scale. In recent years we’ve seen several cloud focused data breaches and evidence shows that threat actors are becoming more advanced with their techniques, goals, and tooling. With 2021 set to be a new high for the number of data breaches, it was plainly evident that we needed to evolve how we approach our cloud infrastructure security strategy.

In 2020, we decided to reinvent how we handle cloud security findings by redefining how we write and respond to cloud detections. We knew that given our scale, we needed to rely heavily on automations and that we needed to build our solutions using battle tested scalable infrastructure.

开源微服务编排框架:Netflix Conductor

本文主要介绍netflix conductor的基本概念和主要运行机制。

Building confidence in a decision

This is the fifth post in a multi-part series on how Netflix uses A/B tests to inform decisions and continuously innovate on our products. Need to catch up? Have a look at Part 1 (Decision Making at Netflix), Part 2 (What is an A/B Test?), Part 3 (False positives and statistical significance), and Part 4 (False negatives and power). Subsequent posts will go into more details on experimentation across Netflix, how Netflix has invested in infrastructure to support and scale experimentation, and the importance of developing a culture of experimentation within an organization.

In Parts 3 (False positives and statistical significance) and 4 (False negatives and power), we discussed the core statistical concepts that underpin A/B tests: false positives, statistical significance and p-values, as well as false negatives and power. Here, we’ll get to the hard part: how do we use test results to support decision making in a complex business environment?

The unpleasant reality about A/B testing is that no test result is a certain reflection of the underlying truth. As we discussed in previous posts, good practice involves first setting and understanding the false positive rate, and then designing an experiment that is well powered so it is likely to detect true effects of reasonable and meaningful magnitudes. These concepts from statistics help us reduce and understand error rates and make good decisions in the face of uncertainty. But there is still no way to know whether the result of a specific experiment is a false positive or a false negative.

Netflix Video Quality at Scale with Cosmos Microservices

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. In particular, the VMAF metric lies at the core of improving the Netflix member’s streaming video quality. It has become a de facto standard for perceptual quality measurements within Netflix and, thanks to its open-source nature, throughout the video industry.

As VMAF evolves and is integrated with more encoding and streaming workflows within Netflix, we need scalable ways of fostering video quality innovations. For example, when we design a new version of VMAF, we need to effectively roll it out throughout the entire Netflix catalog of movies and TV shows. This article explains how we designed microservices and workflows on top of the Cosmos platform to bolster such video quality innovations.

Interpreting A/B test results: false negatives and power

This is the fourth post in a multi-part series on how Netflix uses A/B tests to inform decisions and continuously innovate on our products. Need to catch up? Have a look at Part 1 (Decision Making at Netflix), Part 2 (What is an A/B Test?), Part 3 (False positives and statistical significance). Subsequent posts will go into more details on experimentation across Netflix, how Netflix has invested in infrastructure to support and scale experimentation, and the importance of the culture of experimentation within Netflix.

In Part 3: False positives and statistical significance, we defined the two types of mistakes that can occur when interpreting test results: false positives and false negatives. We then used simple thought exercises based on flipping coins to build intuition around false positives and related concepts such as statistical significance, p-values, and confidence intervals. In this post, we’ll do the same for false negatives and the related concept of statistical power.

Revisiting BetterTLS: Certificate Path Building

Last year the AddTrust root certificate expired and lots of clients had a bad time. Some Roku devices weren’t working right, Heroku had problems, and some folks couldn’t even curl. In the aftermath Ryan Sleevi wrote a really great blog post not just about the issue of this one certificate’s expiry, but the problem that so many TLS implementations have in general with certificate path building. If you haven’t read that blog post, you should. This post is probably going to make a lot more sense if you’ve read that one first, so go ahead and read it now.

CAMBI, a banding artifact detector

Introducing the banding artifacts detector developed by Netflix aiming at further improving the delivered video quality.

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