Netflix Engineering Blog & Tech Articles
Global streaming entertainment service building innovative technology for content delivery, recommendation systems, and cloud infrastructure at scale
601 engineering articles, tutorials, and technical insights from Netflix's engineering team
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By Amer Hesson, Marcelo Mayworm, James Mulcahy, and Brittany Truong
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Netflix introduces Spin, a new feature in Metaflow 2.
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Netflix introduces Advantage-Weighted Supervised Fine-Tuning (A-SFT), a novel post-training algorithm for generative recommender systems that addresses the unique challenges of applying reinforceme...
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Netflix engineered a real-time recommendation delivery system for live events that can update over 100 million devices in under a minute.
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Netflix built a Real-Time Distributed Graph (RDG) to connect member interaction data across their expanding business verticals including streaming, live events, and mobile games.
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Netflix redesigned Maestro's internal workflow engine, replacing the legacy Conductor 2. x-based stateless worker model with a custom stateful actor model built on Java 21 virtual threads.
This article discusses how Netflix built a resilient data platform using a Write-Ahead Log (WAL) to address data consistency, reliability, and operational efficiency challenges at scale.
The article discusses how Netflix scales its Muse application to provide data-driven creative insights at a massive scale, focusing on the architectural evolution and optimizations made to handle t...
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The article discusses Netflix's transformation of incident management from a centralized function to a democratized practice, emphasizing the importance of reliability in delivering seamless entert...
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This article discusses the development of a reliable cloud live streaming pipeline for Netflix, focusing on the challenges faced and solutions implemented to ensure high-quality live streaming expe...
The article discusses the evolution of Netflix's client-server GraphQL APIs, focusing on how they enable dynamic page updates for personalized user experiences.
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The article discusses the evolution of data engineering at Netflix, focusing on the introduction of Media ML Data Engineering, which aims to enhance the handling of complex media data for machine l...
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The article discusses the significance of ML observability at Netflix, emphasizing its role in monitoring and understanding machine learning models, particularly in payment processing.
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The article discusses how Netflix has developed an automated quality control method for video content that detects pixel-level artifacts, significantly reducing the need for manual reviews.
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The article discusses Netflix's journey in implementing live streaming technology over the past three years, detailing the architectural decisions, challenges, and innovations that have led to succ...