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.

Evolution of ML Fact Store

Senior Software Engineer, Machine Learning Platform

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.

Building confidence in a decision

Data Science Manager - Streaming Video & Studio 

Director, Experimentation Platform

In the second in the analytics branding series, Rocio shares her perspective of the projects, technical skills, and day-to-day of working in the Content analytics and metrics research space.

A Day in the Life of a Content Analytics Engineer

Senior Analytics Engineer

This Q&A provides insights into the diverse set of skills, projects, and culture within Data Science and Engineering at Netflix through the eyes of two team members: Chris Pham and Julie Beckley.

How Our Paths Brought Us to Data and Netflix

Senior Data Scientist

An Introduction to Analytics and Visualization Engineering at Netflix

Analytics at Netflix: Who We Are and What We Do

Manager, Product Content Data Science & Engineering

Manager, Content Delivery Science & Analytics

We discuss some key challenges and approaches Netflix has been using to handle small sample size and limited pre-intervention data in quasi experiments.

Key Challenges with Quasi Experiments at Netflix

Director, Experimentation Platform 

The Netflix experience is powered by a family of ranking algorithms, each optimized for a different purpose. For instance, the Top Picks row on the homepage makes recommendations based on a personalized ranking of videos, and the Trending Now row also incorporates recent popularity trends. These algorithms, along with many others, are used together to construct personalized homepages for over 100 million members.

Innovating Faster on Personalization Algorithms at Netflix Using Interleaving

Director, Experimentation

Manager, Experimentation

Manager, Algorithms Science & Analytics

Computational causal inference brings a software implementation focus to causal inference, especially in regards to high performance numerical computing.

Computational Causal Inference

Senior Modeling Architect, Computational Causal Inference at Netflix

In 2012, Netflix launched its first original title to members in Canada and the US. Lilyhammer, starring Steve Van Zandt, was a fish out of water tale about a New York mobster hiding out in rural Norway. As a data scientist starting out my career at Netflix, it felt like an earth-shattering event. Netflix … launching its own original content! It was my role to design and analyze the foundational A/B Test in order to optimize the way we promoted a Netflix show to our members within our service. I felt very Hollywood and very Silicon Valley at the same time. Fast forward to May 8th, 2017 when (within a single week) Netflix debuted about a dozen new original titles to 100 million members in almost 190 countries simultaneously. An amazing transformation - and we’ve only just begun!

Data Science @ Netflix & Promoting Netflix Originals

Vice President, Content and Marketing Science and Analytics

With a global launch in January 2016 that brought Netflix to over 130 new countries, Netflix is now a truly global Internet TV network, available in almost every part of the world. Our member base is over 100 million strong, and approximately half of our members live outside the US. 

How do we ensure that the Netflix streaming experience is as enjoyable in São Paulo, Mumbai, and Bangkok as it is in San Francisco, London, or Paris? The engineers and scientists at Netflix continuously innovate to ensure that we can provide the best QoE possible. To enable this, Netflix has a culture of experimentation and data-driven decision making that allows new ideas to be tested in production so we get data and feedback from our members.

A/B Testing and Beyond: Improving the Netflix Streaming Experience

Director, Studio Production & Streaming Data Science

This blog post describes our approach to predictive CPU isolation for containers at Netflix -- enabling tasks to run faster, and in a more predictable way, by limiting  co-location with noisy neighbors. This approach blends Machine Learning (quantile regression) and optimization techniques (Mixed-Integer Programming).

Predictive CPU isolation of containers at Netflix

Senior Machine Learning Researcher

Senior Software Engineer

We discuss salient challenges of building a search experience for a streaming media service such as Netflix. We provide an overview of the role of recommendations within the search context to aid content discovery and support searches for unavailable (out-of-catalog) entities. We also stress the importance of keystroke-level Instant Search experience, and the technical challenges associated with implementing it across different devices and languages for a global audience.

Challenges in Search on Streaming Services: Netflix Case Study

Senior Research Engineer

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.

Experimentation is a major focus of Data Science across Netflix

Manager, Payments Data Science and Engineering

Perfecting video adaptive bitrate algorithm (ABR) in a commercial setting is challenging, given the endless array of network environments, device capabilities, and content properties. To identify shortcomings effectively, a scalable methodology is needed to evaluate ABR algorithms under various scenarios. The state-of-the-art method is to evaluate a production ABR retrospectively with an optimal ABR algorithm. However, optimal ABR is an NP-hard problem and therefore is costly to be deployed. In this paper, we present Hindsight, an efficient approximation for the optimal ABR problem. We will also share how Hindsight is used as a tool to identify sessions with suboptimal ABR performance in our production environment. This task was previously infeasible at scale due to its high computational complexity. We hope the discussion in
this work can draw attention from the community and help further advance the understanding of this area.

Hindsight: Evaluate Video Bitrate Adaptation at Scale

Engineering Manager, Adaptive Streaming

Manager, Streaming Data Science & Analytics

Senior Software Engineer, Video Algorithms and Research 

Over 69 million Netflix members stream billions of hours of movies and shows every month in North and South America, parts of Europe and Asia, Australia and New Zealand. Soon, Netflix will be available in every corner of the world with an even more global member base.

As we expand globally, our goal is to ensure that every member has a high-quality experience every time they stream content on Netflix. This challenging problem is impacted by factors that include quality of the member’s Internet connection, device characteristics, content delivery network, algorithms on the device, and quality of content.

Optimizing Content Quality Control at Netflix with Predictive Modeling

’Tis the season for holiday binging. How do seasonal viewing patterns affect how Netflix stores and streams content?

Our solution for delivering streaming content is Open Connect, a custom-built system that distributes and stores the audio and video content our members download when they stream a Netflix title (a movie or episode). Netflix has a unique library of large files with relatively predictable popularity, and Open Connect’s global, distributed network of caching servers was designed with these attributes in mind. 

Caching Content for Holiday Streaming

In the internet-connected world, it's almost expected that any form of entertainment (movies, TV shows, sports, music, etc.) is available anytime, anywhere, and on any device. We're in the midst of a significant change in the way we consume content. For example, as a result of new models of content distribution, you no longer have a 13-week wait to find out what happens at the end of a House of Cards season. Instead, you can binge-watch the entire season in one sitting! At the core of this cultural shift is the ability to smoothly stream video over the internet to millions of members around the globe.

Streaming Science at Netflix: Turning Data into a Better Viewing Experience

At Netflix, the Content Platform Engineering and Global Product Creative teams know that imagery plays an incredibly important role in how viewers find new shows and movies to watch. We take pride in surfacing the unique elements of a story that connect our audiences to diverse characters and story lines. As our Original content slate continues to expand, our technical experts are tasked with finding new ways to scale our resources and alleviate our creatives from the tedious and ever-increasing demands of digital merchandising. 

AVA: The Art and Science of Image Discovery at Netflix

Senior UI Architect

Senior Product Designer

Senior Software Engineer - Computer Vision

Senior Software Engineer in Test

Design Program Manager

Program Manager, ML Automation & Systems Infrastructure

In this two-part blog post, we will open the doors of one of the most valued Netflix assets: our recommendation system. In Part 1, we will relate the Netflix Prize to the broader recommendation challenge, outline the external components of our personalized service, and highlight how our task has evolved with the business.

Netflix Recommendations: Beyond the 5 stars (Part 1)

Director, Recommendation Systems Research and Engineering

In this second part of the blog post, we will give more insight into our broader personalization technology. We will discuss some of our current models, data, and the approaches we follow to lead innovation and research in this space.

Netflix Recommendations: Beyond the 5 stars (Part 2)

Having launched the Netflix service globally in January, we now support search in 190 countries. We currently support 20 languages, and this will continue to grow over time. Some of the most challenging language support was added while launching in Japan and Korea as well as in the Chinese and Arabic speaking countries. We have been working on tuning the language specific search prior to each launch by creating and tuning the localized datasets of the documents and their corresponding queries. While targeting a high recall for the launch of a new language, our ranking systems focus on increasing the precision by ranking the most relevant results high on the list.

Global Languages Support at Netflix

Today, in partnership with Google, we have open sourced Kayenta, a platform for Automated Canary Analysis (ACA). Kayenta leverages lessons learned over the years of delivering rapid and reliable changes into production at Netflix. It is a crucial component of delivery at Netflix as it reduces the risk from making changes in our production environment. In addition, Kayenta has increased developer productivity by providing engineers with a high degree of trust in their deployments.

Automated Canary Analysis at Netflix with Kayenta

Netflix has over 139M members streaming on more than half a billion devices spanning over 1,700 different types of devices from hundreds of brands. This diverse device ecosystem results in a high dimensionality feature space, often with sparse data, and can make identifying device performance issues challenging. 

Detecting Performance Anomalies in External Firmware Deployments

Senior Data Scientist at Netflix

Using experimentation as a mechanism to safely and quickly update Netflix client applications.

Safe Updates of Client Applications at Netflix

Engineering Leader

Learn more about how Netflix uses data science and analytics to improve the reliability, security and efficiency of its infrastructure.

How Data Inspires Building a Scalable, Resilient and Secure Cloud Infrastructure At Netflix

Manager - Science & Analytics at Netflix

Cloud Infra and Security Data Engineering Leader at Netflix

Today we’re exploring another important piece of the puzzle: how to create a software architecture that can deliver this experience and support rapid innovation. Coming up with a software architecture that handles large volumes of existing data, is responsive to user interactions, and makes it easy to experiment with new recommendation approaches is not a trivial task. In this post we will describe how we address some of these challenges at Netflix.

System Architectures for Personalization and Recommendation

Providing high-fidelity, near real-time data is important for multi-armed bandit and RL models to converge quickly. In this talk, we will focus on the practical details of leveraging Flink to process trillions of events per day, work with the time dimension, and manage large and frequently-changing state.


Real-time Processing with Flink for Machine Learning at Netflix

What's deeper than deep learning? A look at how recommender systems have grown from matrix factorization, to deep learning, to hyperbolic neural networks.

Deeper Things

Director, Machine Learning

Both router buffer sizing and Active Queue Management (AQM) are concepts deeply embedded inside the computer networks, such that people don't usually draw connections between them and video quality of experience (QoE). In this talk, we will describe our observations at Netflix on how buffer sizing and AQM affects QoE. We hope to draw attention from the community to them through this talk. 

https://ripe78.ripe.net/archives/video/128/



Buffer Sizing and AQM Observations at Netflix

Network Architect

Content Delivery Operations Engineer

Engineering Manager

As we’ve described in our previous blog posts, at Netflix we use personalization extensively and treat every situation as an opportunity to present the right content to each of our over 57 million members. The main way a member interacts with our recommendations is via the homepage, which they see when they log into Netflix on any supported device. The primary function of the homepage is to help each member easily find something to watch that they will enjoy. A problem we face is that our catalog contains many more videos than can be displayed on a single page and each member comes with their own unique set of interests. Thus, a general algorithmic challenge becomes how to best tailor each member’s homepage to make it relevant, cover their interests and intents, and still allow for exploration of our catalog.

Learning a Personalized Homepage

Senior Machine Learning Engineer

Have you ever wondered where your video comes from when you watch Netflix? We serve video streams out of our own content delivery network (CDN), called Open Connect, which is tailored to one specific application: delivering internet TV to our members around the world. This system is the cornerstone of every Netflix video experience — serving 100% of our video, over 125 million hours every day, to 100 million members across the globe! In this post, we introduce some of the challenges in the content-delivery space where our data science and engineering teams collaborate to optimize the Netflix service.

How Data Science Helps Power Worldwide Delivery of Netflix Content

Another day, another custom script to analyze an A/B test. Maybe you’ve done this before and have an old script lying around. If it’s new, it’s probably going to take some time to set up, right? Not at Netflix.

Reimagining Experimentation Analysis at Netflix

Scientific Product Manager, Experimentation Platform

As we have described previously on this blog, at Netflix we are constantly innovating by looking for better ways to find the best movies and TV shows for our members. When a new algorithmic technique such as Deep Learning shows promising results in other domains (e.g. Image Recognition, Neuro-imaging, Language Models, and Speech Recognition), it should not come as a surprise that we would try to figure out how to apply such techniques to improve our product. In this post, we will focus on what we have learned while building infrastructure for experimenting with these approaches at Netflix.

Distributed Neural Networks with GPUs in the AWS Cloud

We are pleased to announce the open-source launch of Polynote: a new, polyglot notebook with first-class Scala support, Apache Spark integration, multi-language interoperability including Scala, Python, and SQL, as-you-type autocomplete, and more.

Open-sourcing Polynote: an IDE-inspired polyglot notebook

Senior Software Engineer, Machine Learning

Director of Engineering, Personalization Infrastructure

Last January, Netflix launched globally, reaching many new members in 130 countries around the world. In many of these countries, people access the internet primarily using cellular networks or still-developing broadband infrastructure. 

More Efficient Mobile Encodes for Netflix Downloads

Senior Research Scientist

Senior Software Engineer, Video Algorithms

Director, Video Algorithms

Invited talk at the Adaptive and Multi-Task Learning (AMTL) workshop at ICML 2019 on 2019-06-15 in Long Beach, CA. Covers some recent trends in research in the personalization and recommender systems space along with some potential opportunities for using multi-task and meta learning in that area.


Recent Trends in Personalization: A Netflix Perspective

In order to produce software that improves a scientist's productivity we have come up with the following design principles.

Design Principles for Mathematical Engineering in Experimentation Platform at Netflix

On January 16, 2007, Netflix started rolling out a new feature: members could now stream movies directly on their browser without having to wait for the red envelope in the mail. This event marked a substantial shift for Netflix and the entertainment industry. A lot has changed since then. Today, Netflix delivers over 1 billion hours of streaming per month to 48 million members in more than 40 countries. And Netflix accounts for more than a third of peak Internet traffic in the US. This level of engagement results in a humungous amount of data.

Optimizing the Netflix Streaming Experience with Data Science

When Netflix launched in 130 additional countries in January, we wanted our service to work well everywhere in the world, and that meant solving many complex technical problems with a global approach.

For example, the personalized recommendations we offer members are central to the Netflix experience, helping people find something great to watch quickly and easily. In general, our algorithms analyze vasts amounts of data to produce these recommendations, and making this process work globally was challenging on several fronts.

A Global Approach to Recommendations

With 83+ million members watching billions of hours of TV shows and movies, Netflix sends a huge amount of video bits through the Internet. As we grow globally, more of these video bits will be streamed through bandwidth-constrained cellular networks. Our team works on improving our video compression efficiency to ensure that we are good stewards of the Internet while at the same time delivering the best video quality to our members. Part of the effort is to evaluate the state-of-the-art video codecs, and adopt them if they provide substantial compression gains.



A Large-Scale Comparison of x264, x265, and libvpx — a Sneak Peek

We’ve spent years developing an approach, called per-title encoding, where we run analysis on an individual title to determine the optimal encoding recipe based on its complexity. Imagine having very involved action scenes that need more bits to encapsulate the information versus unchanging landscape scenes or animation that need less. This allows us to deliver the same or better experience while using less bandwidth, which will be particularly important in lower bandwidth countries and as we expand to places where video viewing often happens on mobile networks.

Per-Title Encode Optimization

 Senior Software Engineer, Video Algorithms

Director of Encoding Technologies

At Netflix we care about video quality, and we care about measuring video quality accurately at scale. Our method, Video Multimethod Assessment Fusion (VMAF), seeks to reflect the viewer’s perception of our streaming quality. We are open-sourcing this tool and invite the research community to collaborate with us on this important project.

Toward A Practical Perceptual Video Quality Metric

Video encoding has fueled academic research over the past 25 years and enabled compelling products and services. Many companies are built around video encoding and transmission — Netflix and Google’s YouTube are two prime examples of video-centric companies. The fundamentals of video encoding haven’t changed for all these years, as modern video streams are produced with the same type of encoding parameters that have been used since MPEG-1: a certain frame resolution is chosen, together with a group-of-pictures (GOP) structure that imposes periodic Intra pictures, and a target bitrate that is (approximately) met by either a single-pass or two-passes over the input video frames.

Dynamic optimizer — a perceptual video encoding optimization framework

A key aspect of Netflix is providing our members with a personalized experience so they can easily find great stories to enjoy. A collection of recommender systems drive the main aspects of this personalized experience and we continuously work on researching and testing new ways to make them better. As such, we were delighted to sponsor and participate in this year’s ACM Conference on Recommender Systems in Boston, which marked the 10th anniversary of the conference. For those who couldn’t attend or want more information, here is a recap of our talks and papers at the conference.

Netflix at RecSys 2016 Recap

Director, Algorithm Engineering

Because treatments in streaming experimentation may have much larger impacts on high (or low) values of a given metric, changes in the mean, the median, or other summary statistics are not generally sufficient to understand if and how the test treatment has changed the behaviour of that metric. In general, we are interested in how the distributions of relevant metrics differ between test experiences (called “cells” here at Netflix). Our goal is to arrive at an inference and visualization solution that simultaneously indicates which parts of the distribution have changed, and both the practical and statistical significance of those changes.

Streaming Video Experimentation at Netflix: Visualizing Practical and Statistical Significance

A recap of the ML Platform meetup hosted at Netflix, Los Gatos on Sep 12, 2019 on the topic of Infrastructure for Contextual Bandits and Reinforcement Learning. Features slides and videos from LinkedIn, Netflix, Facebook, and Dropbox


ML Platform Meetup: Infra for Contextual Bandits and Reinforcement Learning

We want to make it easy for Netflix members to find great content to fulfill their unique tastes. To do this, we follow a data-driven algorithmic approach based on machine learning, which we have described in past posts and other publications. We aspire to a day when anyone can sit down, turn on Netflix, and the absolute best content for them will automatically start playing.

Distributed Time Travel for Feature Generation

Engineering Manager, Data Systems for Personalization

With the deluge of deep learning libraries and software innovation in the field over the last few years, it is an exciting time to be working on machine learning problems. Most of the libraries available evolved from fairly specialized computational code for large dense problems such as image classification into general frameworks for neural-network-based models offering marginal support for sparse models.



Introducing Vectorflow: a lightweight neural network library for sparse data

Machine Learning is making fast inroads into many areas of business and is being employed in an increasingly widening array of commercial applications. At Netflix, ML has been used for several years on our Recommendations and Personalization problems. Over the last couple of years, ML has expanded into a wide range of newer applications here, such as Content Promotion, Content Price Modeling, Programmatic Marketing, Efficient Content Delivery, Adaptive Streaming QoE, to name only a handful.

Machine Learning Platform Meetup Recap

We’re developing a new product within the wider Experimentation Platform, to automate some aspects of the scientists workflow so we can free up our scientists and run more quasi-experiments in parallel. 

Quasi Experimentation at Netflix

Senior Product Manager, Experimentation Platform

In January 2016 we launched our service globally to begin bringing joy to a much larger audience.  Marketing drives awareness, original content drives organic growth, and we actively engage in partnerships to create inroads to new member bases.  Our partnerships include consumer electronics companies, cable TV operators, mobile operators, and of course Apple and Google. No matter where you are in the world, a partner is helping bring the same amazing Netflix experience to you.

Scaling Netflix through Partnerships

Manager, GPP S&A Data Products

It’s 2 a.m. and half of our reliability team is online searching for the root cause of why Netflix streaming isn’t working. None of our systems are obviously broken, but something is amiss and we’re not seeing it. After an hour of searching we realize there is one rogue server in our farm causing the problem. We missed it amongst the thousands of other servers because we were looking for a clearly visible problem, not an insidious deviant.

Tracking down the Villains: Outlier Detection at Netflix

Director of Engineering, Discovery & Playback Services

Senior Reliability Engineer

Netflix has released hundreds of Originals and plans to spend $8 billion over the next year on content. Creators of these stories pour their hearts and souls into turning ideas into joy for our viewers. The sublime art of doing this well is hard to describe, but it necessitates a careful orchestration of creative, business and technical decisions. Here we will focus on the latter two — business & technical decisions like planning budgets, finding locations, building sets, and scheduling guest actors that enable the creative act of connecting with viewers.

Data Science and the Art of Producing Entertainment at Netflix

Director, Science & Analytics

Manager, Science & Analytics

Senior Machine Learning Scientist

At Netflix, we regularly run experiments to improve the quality of our member’s streaming service. We rely on custom bootstrapping methods to analyze results and understand movements in our metrics. Our methods have been well adopted by our engineering partners for a variety of reasons: they are flexible, avoid distributional assumptions, and let us understand changes in different parts of metric distributions. In order to run these methods on hundreds of experiments and massive datasets, we need to efficiently compress the data to increase the speed of compute while maintaining highly accurate results. We will go through our compression mechanism, provide intuition on why it works, and summarize the gains in speed.

Data Compression for Large-Scale Streaming Experimentation

Full Stack Data Scientist

From living rooms in Bogota, to morning commutes in Tokyo, to beaches in Los Angeles and dorms in Berlin, Netflix strives to bring joy to over 139 million members around the globe and connect people with stories they’ll love

Improving Experimentation Efficiency at Netflix with Meta Analysis and Optimal Stopping

Senior Data Scientist 

Director, Growth, Payments & Partnerships Science & Analytics 

One of the common questions we get asked is: “Why do we need machine learning to improve streaming quality?” This is a really important question, especially given the recent hype around machine learning and AI which can lead to instances where we have a “solution in search of a problem.” In this blog post, we describe some of the technical challenges we face for video streaming at Netflix and how statistical models and machine learning techniques can help overcome these challenges.

Using Machine Learning to Improve Streaming Quality at Netflix

Sean shares his perspective on how nonlinear career trajectories make us better, and can bring unexpected benefits during unforeseen circumstances.

My (Seemingly) Random Walk to Netflix

The Netflix experience is driven by a number of Machine Learning algorithms: personalized ranking, page generation, search, similarity, ratings, etc. On the 6th of January, we simultaneously launched Netflix in 130 new countries around the world, which brings the total to over 190 countries. Preparing for such a rapid expansion while ensuring each algorithm was ready to work seamlessly created new challenges for our recommendation and search teams. In this post, we highlight the four most interesting challenges we’ve encountered in making our algorithms operate globally and, most importantly, how this improved our ability to connect members worldwide with stories they’ll love.

Recommending for the World

Our objective in improving the Netflix recommendation system is to create a personalized experience that makes it easier for our members to find great content to enjoy. The ultimate goal of our recommendation system is to know the exact perfect show for the member and just start playing it when they open Netflix. While we still have a long way to achieve that goal, there are areas where we can reduce the gap significantly.

To Be Continued: Helping you find shows to continue watching on Netflix

Manager, Discovery Research

For many years, the main goal of the Netflix personalized recommendation system has been to get the right titles in front each of our members at the right time. With a catalog spanning thousands of titles and a diverse member base spanning over a hundred million accounts, recommending the titles that are just right for each member is crucial. But the job of recommendation does not end there. Why should you care about any particular title we recommend? What can we say about a new and unfamiliar title that will pique your interest? How do we convince you that a title is worth watching? 

Artwork Personalization at Netflix

Director, Machine Learning and Personalization Science

The paper gives a technical overview of AV1 - the first video codec standard from Aliance for Open Media (AOM).


An Overview of Coding Tools in AV1: the First Video Codec from the Alliance for Open Media

In this tech blog, we report the current status of the SVT-AV1 project, as well as the characteristics and performance of the encoder and decoder.

SVT-AV1: open-source AV1 encoder and decoder

Senior Research Scientist - Video Encoding

Research Scientist - Video Encoding

Senior Software Engineer - Video Algorithms

Software Engineer

SVT-AV1 is a open-source AV1 framework born out of the collaboration between Intel and Netflix. In this tech blog, we describe the relevance of this partnership to the industry and cover some of our own experiences.

Introducing SVT-AV1: a scalable open-source AV1 framework

We need an alternative to JPEG that a) is widely supported, b) has better compression efficiency and c) has a wider feature set. We believe AV1 Image File Format (AVIF) has the potential. Using the framework we have open sourced, AVIF compression efficiency can be seen at work and compared against a whole range of image codecs that came before it.

AVIF for Next-Generation Image Coding

Senior Software Engineer - Cloud Media Systems

Describes the latest progress towards Netflix's open-source video quality assessment metric.

VMAF: The Journey Continues

Software Engineer - Video Algorithms

Bad picture quality — blockiness, blurring, distorted faces and objects — can draw you out of that favorite TV show or movie you’re watching on Netflix. In many cases, low bandwidth networks or data caps prevent us from delivering the perfect picture. To address this, the Netflix Video Algorithms team has been working on more efficient compression algorithms that enable Netflix to deliver the same or better picture quality while using less bandwidth. And working together with other engineering teams at Netflix, we update our client applications and streaming infrastructure to support the new video streams and to ensure seamless playback on Netflix devices.

Optimized shot-based encodes: Now Streaming!

Is image and video coding dead? From the standpoint of application and relevance, video compression is very much alive and kicking and thriving on the internet.

The End of Video Coding?

In the third installment of the Netflix analytics series, Alex helps mythbust some common misconceptions about succeeding in analytics at a big tech company.

Publications

Netflix Research regularly publishes a variety of updates, talks, and other presentations.

Recent Articles

On Negative Sampling for Audio-Visual Contrastive Le...

Evolution of ML Fact Store

Deep Learning for Recommender Systems: A Netflix Ca...

Beyond Customer Lifetime Valuation: Measuring the Va...

Building confidence in a decision

Experimentation is a major focus of Data Science acr...

Browse Content