Essential Suite — Artwork Producer Assistant

Introduction Netflix continues to invest in content for a global audience with a diverse range of unique tastes and interests. Correspondingly, the member experience must also evolve to connect this global audience to the content that most appeals to each of them. Images that represent…

ML Platform Meetup: Infra for Contextual Bandits and Reinforcement Learning

Infrastructure for Contextual Bandits and Reinforcement Learning — theme of the ML Platform meetup hosted at Netflix, Los Gatos on Sep 12, 2019. Contextual and Multi-armed Bandits enable faster and adaptive alternatives to traditional A/B Testing. They enable rapid learning and better decision-making for product rollouts. Broadly speaking, these approaches…

Trace Event, Chrome and More Profile Formats on FlameScope

Less than a year ago, FlameScope was released as a proof of concept for a new profile visualization. Since then, it helped us, and many other users, to easily find and fix performance issues, and allowed us to see patterns that we had never noticed before in our profiles. As…

Computational Causal Inference at Netflix

Every Netflix data scientist, whether their background is from biology, psychology, physics, economics, math, statistics, or biostatistics, has made meaningful contributions to the way Netflix analyzes causal effects. Scientists from these fields have made many advancements in causal effects research in the past few decades, spanning…

Netflix at Spark+AI Summit 2018

A glimpse at Spark usage for Netflix Recommendations Apache Spark has been an immensely popular big data platform for distributed computing. Netflix has been using Spark extensively for various batch and stream-processed workloads. A substantial list of use cases for Spark computation come from the various applications in the domain of content recommendations and personalization…

Edgar: Solving Mysteries Faster with Observability

by Elizabeth Carretto Our Team — Kevin Lew, Maulik Pandey, Narayanan Arunachalam, Dustin Haffner, Andrei Ushakov, Seth Katz, Greg Burrell, Ram Vaithilingam, Mike Smith and Elizabeth Carretto Everyone loves Unsolved Mysteries. There’s always someone who seems like the surefire culprit. There’s a clear motive, the perfect opportunity, and an…

Improving our video encodes for legacy devices

Netflix has been one of the pioneers of streaming video-on-demand content — we announced our intention to stream video over 13 years ago, in January 2007 — and have only increased both our device and content reach since then…

Python at Netflix

As many of us prepare to go to PyCon, we wanted to share a sampling of how Python is used at Netflix. We use Python through the full content lifecycle, from deciding which content to fund all…

Quasi Experimentation at Netflix

Experimentation informs much of our decision making at Netflix. We design, analyze, and execute experiments with rigor so that we have confidence that the changes we’re making are the right ones for our members and our business. We have many years of experience running…

Growth Engineering at Netflix — Automated Imagery Generation

Background There’s a good chance you’ve probably visited the Netflix homepage. In the Growth Engineering team, we refer to this as the top of the signup funnel. For more background on the signup funnel and Growth Engineering’s role in the signup funnel, please read our initial post on…

Building a Rule-Based Platform to Manage Netflix Membership SKUs at Scale

At Netflix, we aspire to entertain the world. From mailing DVDs in the US to a global streaming service with over 200 million subscribers across 190 countries, we have come a long way. For the longest time, Netflix had three plans (basic/standard/premium)…

How Netflix uses Druid for Real-time Insights to Ensure a High-Quality Experience

Ensuring a consistently great Netflix experience while continuously pushing innovative technology updates is no easy feat. How can we be confident that updates are not harming our users? And that we’re actually making measurable improvements when we intend to? Using real-time logs from playback devices as a…

My (Seemingly) Random Walk to Netflix

By Sean Barnes, Studio Production Data Science & Engineering I am going to tell you a story about a person that works for Netflix. That person grew up dreaming of working in the entertainment industry. They attended the University of Southern California, double majored in data science and television &…

Packaging award-winning shows with award-winning technology

Introduction In previous blog posts, our colleagues at Netflix have explained how 4K video streams are optimized, how even legacy video streams are improved and more recently how new audio codecs can provide better aural experiences to our members. In all these cases, prior to being delivered through…

The End of Video Coding?

In the IEEE Signal Processing Magazine issue November 2006 article “Future of Video Coding and Transmission” Prof. Edward Delp started by asking the panelists “Is video coding dead? Some feel that, with the higher coding efficiency of the H.264/MPEG-4 . . . perhaps there is not much more to do…

A Day in the Life of an Experimentation and Causal Inference Scientist @ Netflix

Source credit: Netflix Within the rapid expansion of data-related roles in the last decade, the title Data Scientist has emerged as an umbrella term for myriad skills and areas of business focus. What does this title mean within a given company, or even within a given industry…

Pseudo Localization @ Netflix

In the past 8 years Netflix has transformed from an English-only product, to now supporting 26 languages and growing. As we add language support for our members residing in 190 different countries, scaling globalization at Netflix has never been more important. Along the way we’ve built out…

Netflixにおける日本語字幕の導入

Netflixでは、2015年9月の日本における配信サービス開始時から日本語字幕を提供して …

Introducing Dispatch

Netflix is pleased to announce the open-source release of our crisis management orchestration framework: Dispatch! Okay, but what is Dispatch? Put simply, Dispatch is: All of the ad-hoc things you’re doing to manage incidents today, done for you, and a bunch of other things you should’ve been doing, but have not had the time! Dispatch helps us effectively…

Design Principles for Mathematical Engineering in Experimentation Platform at Netflix

At Netflix, we have data scientists coming from many backgrounds such as neuroscience, statistics and biostatistics, economics, and physics; each of these backgrounds has a meaningful contribution to how experiments should be analyzed. To unlock these innovations we…

Applying Netflix DevOps Patterns to Windows

By Justin Phelps and Manuel Correa Customizing Windows images at Netflix was a manual, error-prone, and time consuming process. In this blog post, we describe how we improved the methodology, which technologies we leveraged, and how this has improved service deployment and consistency. Artisan Crafted Images In the Netflix…

Open Sourcing Mantis: A Platform For Building Cost-Effective, Realtime, Operations-Focused…

Today we’re excited to announce that we’re open sourcing Mantis, a platform that helps Netflix engineers better understand the behavior of their applications to ensure the highest quality experience for our members. We…

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

Netflix’s engineering culture is predicated on Freedom & Responsibility, the idea that everyone (and every team) at Netflix is entrusted with a core responsibility and they are free to operate with freedom to satisfy their mission. This…

Netflix Public Bug Bounty, 1 year later

As Netflix continues to create entertainment people love, the security team continues to keep our members, partners, and employees secure. The security research community has partnered with us to improve the security of the Netflix service for the past few years through our responsible…

Detecting Performance Anomalies in External Firmware Deployments

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…