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.

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.

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.

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

This is the third 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) and Part 2 (What is an A/B Test?). 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 2: What is an A/B Test we talked about testing the Top 10 lists on Netflix, and how the primary decision metric for this test was a measure of member satisfaction with Netflix. If a test like this shows a statistically significant improvement in the primary decision metric, the feature is a strong candidate for a roll out to all of our members. But how do we know if we’ve made the right decision, given the results of the test? It’s important to acknowledge that no approach to decision making can entirely eliminate uncertainty and the possibility of making mistakes. Using a framework based on hypothesis generation, A/B testing, and statistical analysis allows us to carefully quantify uncertainties, and understand the probabilities of making different types of mistakes.

Quality of a client application is of paramount importance to global digital products, as it is the primary way customers interact with a brand. At Netflix, we have significant investments in ensuring new versions of our applications are well tested. However, Netflix is available for streaming on thousands of types of devices and it is powered by hundreds of micro-services which are deployed independently, making it extremely challenging to comprehensively test internally. Hence, it became important to supplement our release decisions with strong evidence received from the field during the update process.

Our team was formed to mine health signals from the field to quickly evaluate new versions of the client applications. As we invested in systems to enable this vision, it led to increased development velocity, which arguably led to better development practices and quality of the applications. The goal of this blog post is to highlight the investment areas for this vision and the challenges we are facing today.

As we continue to grow here at Netflix, the needs of Revenue and Growth Engineering are rapidly evolving; and our tools must also evolve just as rapidly. The Revenue and Growth Tools (RGT) team decided to set off on a journey to build tools in an abstract manner to have solutions readily available within our organization. We identified common design patterns and architectures scattered across various tools which were all duplicating efforts in some way or another.

We needed to consolidate these tools in a way that scaled with the teams we served. It needed to have the agility of a micro frontend and the extensibility of a framework to empower our stakeholders to extend our tools. We would abstract parts of which anyone can then customize, or extend, to meet their specific business or technical requirements. The end result is Lattice: RGT’s pluggable framework for micro frontends.

Behind the scenes of the beloved Netflix streaming service and content, there are many technology innovations in media processing. Packaging has always been an important step in media processing. After content ingestion, inspection and encoding, the packaging step encapsulates encoded video and audio in codec agnostic container formats and provides features such as audio video synchronization, random access and DRM protection. Our previous tech blog Packaging award-winning shows with award-winning technology detailed our packaging technology deployed on the streaming side.

This introduction is the first in a multi-part series on how Netflix uses A/B tests to make decisions that continuously improve our products, so we can deliver more joy and satisfaction to our members. Subsequent posts will cover the basic statistical concepts underpinning A/B tests, the role of experimentation across Netflix, how Netflix has invested in infrastructure to support and scale experimentation, and the importance of the culture of experimentation within Netflix.

This is the second post in a multi-part series on how Netflix uses A/B tests to inform decisions and continuously innovate on our products. See here for Part 1: Decision Making at Netflix. Subsequent posts will go into more details on the statistics of A/B tests, 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 our previous post, we discussed how we utilize FieldMask as a solution when designing our APIs so that consumers can request the data they need when fetched via gRPC. In this blog post we will continue to cover how Netflix Studio Engineering uses FieldMask for mutation operations such as update and remove.

A Journey About Productizing Security.

At Netflix, we heavily use gRPC for the purpose of backend to backend communication. When we process a request it is often beneficial to know which fields the caller is interested in and which ones they ignore. Some response fields can be expensive to compute, some fields can require remote calls to other services. Remote calls are never free; they impose extra latency, increase probability of an error, and consume network bandwidth. How can we understand which fields the caller doesn’t need to be supplied in the response, so we can avoid making unnecessary computations and remove calls? With GraphQL this comes out of the box through the use of field selectors. In the JSON:API standard a similar technique is known as Sparse Fieldsets. How can we achieve a similar functionality when designing our gRPC APIs? The solution we use within the Netflix Studio Engineering is protobuf FieldMask.

At Netflix, hundreds of different device types, from streaming sticks to smart TVs, are tested every day through automation to ensure that new software releases continue to deliver the quality of the Netflix experience that our customers enjoy. In addition, Netflix continuously works with its partners (such as Roku, Samsung, LG, Amazon) to port the Netflix SDK to their new and upcoming devices (TVs, smart boxes, etc), to ensure the quality bar is reached before allowing the Netflix application on the device to go out into the world. The Partner Infrastructure team at Netflix provides solutions to support these two significant efforts by enabling device management at scale.

复盘Netflix发展历程、产品策略和财务表现对于分析中国国内长视频行业具有一定借鉴意义。

只有当我们用正确的流失数据才有可能通过分析来正确的指导决策。本文介绍的错误都是很常见的哦！​