The Netflix TechBlog

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Supporting Diverse ML Systems at Netflix

The Netflix TechBlog

David J. Berg , Romain Cledat , Kayla Seeley , Shashank Srikanth , Chaoying Wang , Darin Yu Netflix uses data science and machine learning across all facets of the company, powering a wide range of business applications from our internal infrastructure and content demand modeling to media understanding. The Machine Learning Platform (MLP) team at Netflix provides an entire ecosystem of tools around Metaflow , an open source machine learning infrastructure framework we started, to empower data sc

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Bending pause times to your will with Generational ZGC

The Netflix TechBlog

The surprising and not so surprising benefits of generations in the Z Garbage Collector. By Danny Thomas, JVM Ecosystem Team The latest long term support release of the JDK delivers generational support for the Z Garbage Collector. More than half of our critical streaming video services are now running on JDK 21 with Generational ZGC, so it’s a good time to talk about our experience and the benefits we’ve seen.

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Evolving from Rule-based Classifier: Machine Learning Powered Auto Remediation in Netflix Data…

The Netflix TechBlog

Evolving from Rule-based Classifier: Machine Learning Powered Auto Remediation in Netflix Data Platform by Binbing Hou , Stephanie Vezich Tamayo , Xiao Chen , Liang Tian , Troy Ristow , Haoyuan Wang , Snehal Chennuru , Pawan Dixit This is the first of the series of our work at Netflix on leveraging data insights and Machine Learning (ML) to improve the operational automation around the performance and cost efficiency of big data jobs.

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Announcing bpftop: Streamlining eBPF performance optimization

The Netflix TechBlog

By Jose Fernandez Today, we are thrilled to announce the release of bpftop , a command-line tool designed to streamline the performance optimization and monitoring of eBPF applications. As Netflix increasingly adopts eBPF [ 1 , 2 ], applying the same rigor to these applications as we do to other managed services is imperative. Striking a balance between eBPF’s benefits and system load is crucial, ensuring it enhances rather than hinders our operational efficiency.

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Sequential A/B Testing Keeps the World Streaming Netflix Part 1: Continuous Data

The Netflix TechBlog

Michael Lindon , Chris Sanden , Vache Shirikian , Yanjun Liu , Minal Mishra , Martin Tingley 1. Spot the Difference Can you spot any difference between the two data streams below? Each observation is the time interval between a Netflix member hitting the play button and playback commencing, i.e., play-delay. These observations are from a particular type of A/B test that Netflix runs called a software canary or regression-driven experiment.

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Introducing SafeTest: A Novel Approach to Front End Testing

The Netflix TechBlog

by Moshe Kolodny In this post, we’re excited to introduce SafeTest, a revolutionary library that offers a fresh perspective on End-To-End (E2E) tests for web-based User Interface (UI) applications. The Challenges of Traditional UI Testing Traditionally, UI tests have been conducted through either unit testing or integration testing (also referred to as End-To-End (E2E) testing).

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Rebuilding Netflix Video Processing Pipeline with Microservices

The Netflix TechBlog

Liwei Guo , Anush Moorthy , Li-Heng Chen , Vinicius Carvalho , Aditya Mavlankar , Agata Opalach , Adithya Prakash , Kyle Swanson, Jessica Tweneboah , Subbu Venkatrav , Lishan Zhu This is the first blog in a multi-part series on how Netflix rebuilt its video processing pipeline with microservices, so we can maintain our rapid pace of innovation and continuously improve the system for member streaming and studio operations.