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	<title>Netflix Technology Blog &#8211; Noise</title>
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		<title>The AI Evolution of Graph Search at Netflix</title>
		<link>https://noise.getoto.net/2026/01/26/the-ai-evolution-of-graph-search-at-netflix/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Mon, 26 Jan 2026 19:01:27 +0000</pubDate>
				<category><![CDATA[AI]]></category>
		<category><![CDATA[GraphQL]]></category>
		<category><![CDATA[LLM]]></category>
		<category><![CDATA[search engines]]></category>
		<category><![CDATA[software engineering]]></category>
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					<description><![CDATA[The AI Evolution of Graph Search at Netflix: From Structured Queries to Natural LanguageBy Alex Hutter and Bartosz BalukiewiczOur previous blog posts (part 1, part 2, part 3) detailed how Netflix’s Graph Search platform addresses the challenges of sear...]]></description>
		
		
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		<title>How Temporal Powers Reliable Cloud Operations at Netflix</title>
		<link>https://noise.getoto.net/2025/12/16/how-temporal-powers-reliable-cloud-operations-at-netflix/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Mon, 15 Dec 2025 23:51:59 +0000</pubDate>
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					<description><![CDATA[By Jacob Meyers and Rob ZienertTemporal is a Durable Execution platform which allows you to write code “as if failures don’t exist”. It’s become increasingly critical to Netflix since its initial adoption in 2021, with users ranging from the operators ...]]></description>
		
		
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		<title>Netflix Live Origin</title>
		<link>https://noise.getoto.net/2025/12/15/netflix-live-origin/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Mon, 15 Dec 2025 17:38:16 +0000</pubDate>
				<category><![CDATA[Cloud Storage]]></category>
		<category><![CDATA[content delivery network]]></category>
		<category><![CDATA[live streaming]]></category>
		<category><![CDATA[live-origin]]></category>
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					<description><![CDATA[Xiaomei Liu, Joseph Lynch, Chris NewtonIntroductionBehind the Streams: Building a Reliable Cloud Live Streaming Pipeline for Netflix introduced the architecture of the streaming pipeline. This blog post looks at the custom Origin Server we built for Li...]]></description>
		
		
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			</item>
		<item>
		<title>AV1 — Now Powering 30% of Netflix Streaming</title>
		<link>https://noise.getoto.net/2025/12/04/av1-now-powering-30-of-netflix-streaming/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Thu, 04 Dec 2025 20:09:30 +0000</pubDate>
				<category><![CDATA[aomedia]]></category>
		<category><![CDATA[av1]]></category>
		<category><![CDATA[Netflix]]></category>
		<category><![CDATA[streaming]]></category>
		<category><![CDATA[video-encoding]]></category>
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					<description><![CDATA[AV1 — Now Powering 30% of Netflix StreamingLiwei Guo, Zhi Li, Sheldon Radford, Jeff WattsStreaming video has become an integral part of our daily lives. At Netflix, our top priority is delivering the best possible entertainment experience to our member...]]></description>
		
		
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		<item>
		<title>Supercharging the ML and AI Development Experience at Netflix</title>
		<link>https://noise.getoto.net/2025/11/04/supercharging-the-ml-and-ai-development-experience-at-netflix/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Tue, 04 Nov 2025 20:33:44 +0000</pubDate>
				<category><![CDATA[artificial intelligence]]></category>
		<category><![CDATA[Developer Tools]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[metaflow]]></category>
		<category><![CDATA[mlops]]></category>
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					<description><![CDATA[Supercharging the ML and AI Development Experience at Netflix with MetaflowShashank Srikanth, Romain CledatMetaflow — a framework we started and open-sourced in 2019 — now powers a wide range of ML and AI systems across Netflix and at many other compan...]]></description>
		
		
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		<item>
		<title>Post-Training Generative Recommenders with Advantage-Weighted Supervised Finetuning</title>
		<link>https://noise.getoto.net/2025/10/26/post-training-generative-recommenders-with-advantage-weighted-supervised-finetuning/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Sat, 25 Oct 2025 21:32:37 +0000</pubDate>
				<category><![CDATA[AI]]></category>
		<category><![CDATA[large language models]]></category>
		<category><![CDATA[Recommendations]]></category>
		<category><![CDATA[reinforcement-learning]]></category>
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					<description><![CDATA[Author: Keertana Chidambaram, Qiuling Xu, Ko-Jen Hsiao, Moumita Bhattacharya(*The work was done when Keertana interned at Netflix.)IntroductionThis blog focuses on post-training generative recommender systems. Generative recommenders (GRs) represent a ...]]></description>
		
		
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		<item>
		<title>Behind the Streams: Real-Time Recommendations for Live Events Part 3</title>
		<link>https://noise.getoto.net/2025/10/21/behind-the-streams-real-time-recommendations-for-live-events-part-3/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Tue, 21 Oct 2025 00:53:29 +0000</pubDate>
				<category><![CDATA[Architecture]]></category>
		<category><![CDATA[distributed-systems]]></category>
		<category><![CDATA[live streaming]]></category>
		<category><![CDATA[Netflix]]></category>
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					<description><![CDATA[By: Kris Range, Ankush Gulati, Jim Isaacs, Jennifer Shin, Jeremy Kelly, Jason TuThis is part 3 in a series called “Behind the Streams”. Check out part 1 and part 2 to learn more.Picture this: It’s seconds before the biggest fight night in Netflix histo...]]></description>
		
		
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		<item>
		<title>How and Why Netflix Built a Real-Time Distributed Graph: Part 1 — Ingesting and Processing Data…</title>
		<link>https://noise.getoto.net/2025/10/17/how-and-why-netflix-built-a-real-time-distributed-graph-part-1-ingesting-and-processing-data/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Fri, 17 Oct 2025 18:42:37 +0000</pubDate>
				<category><![CDATA[Big Data]]></category>
		<category><![CDATA[data-engineering]]></category>
		<category><![CDATA[software-architecture]]></category>
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					<description><![CDATA[How and Why Netflix Built a Real-Time Distributed Graph: Part 1 — Ingesting and Processing Data Streams at Internet ScaleAuthors: Adrian Taruc and James DaltonThis is the first entry of a multi-part blog series describing how we built a Real-Time Distr...]]></description>
		
		
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			</item>
		<item>
		<title>100X Faster: How We Supercharged Netflix Maestro’s Workflow Engine</title>
		<link>https://noise.getoto.net/2025/09/29/100x-faster-how-we-supercharged-netflix-maestros-workflow-engine/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Mon, 29 Sep 2025 16:10:40 +0000</pubDate>
				<category><![CDATA[data]]></category>
		<category><![CDATA[distributed-systems]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[Orchestration]]></category>
		<category><![CDATA[workflow]]></category>
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					<description><![CDATA[By Jun He, Yingyi Zhang, Ely SpearsTL;DRWe recently upgraded the Maestro engine to go beyond scalability and improved its performance by 100X! The overall overhead is reduced from seconds to milliseconds. We have updated the Maestro open source project...]]></description>
		
		
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			</item>
		<item>
		<title>Building a Resilient Data Platform with Write-Ahead Log at Netflix</title>
		<link>https://noise.getoto.net/2025/09/26/building-a-resilient-data-platform-with-write-ahead-log-at-netflix/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Fri, 26 Sep 2025 18:57:07 +0000</pubDate>
				<category><![CDATA[database]]></category>
		<category><![CDATA[distributed-systems]]></category>
		<category><![CDATA[message-queue]]></category>
		<category><![CDATA[reliability]]></category>
		<category><![CDATA[software-architecture]]></category>
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					<description><![CDATA[By Prudhviraj Karumanchi, Samuel Fu, Sriram Rangarajan, Vidhya Arvind, Yun Wang, John LuIntroductionNetflix operates at a massive scale, serving hundreds of millions of users with diverse content and features. Behind the scenes, ensuring data consisten...]]></description>
		
		
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		<item>
		<title>Scaling Muse: How Netflix Powers Data-Driven Creative Insights at Trillion-Row Scale</title>
		<link>https://noise.getoto.net/2025/09/23/scaling-muse-how-netflix-powers-data-driven-creative-insights-at-trillion-row-scale/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Mon, 22 Sep 2025 21:24:20 +0000</pubDate>
				<category><![CDATA[Big Data]]></category>
		<category><![CDATA[data-engineering]]></category>
		<category><![CDATA[Druid]]></category>
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					<description><![CDATA[By Andrew Pierce, Chris Thrailkill, Victor ChiapaikeoAt Netflix, we prioritize getting timely data and insights into the hands of the people who can act on them. One of our key internal applications for this purpose is Muse. Muse’s ultimate goal is to ...]]></description>
		
		
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		<item>
		<title>Empowering Netflix Engineers with Incident Management</title>
		<link>https://noise.getoto.net/2025/09/19/empowering-netflix-engineers-with-incident-management/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Fri, 19 Sep 2025 16:48:00 +0000</pubDate>
				<category><![CDATA[incident response]]></category>
		<category><![CDATA[incident-management]]></category>
		<category><![CDATA[reliability]]></category>
		<category><![CDATA[site-reliability-engineer]]></category>
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					<description><![CDATA[By: Molly StruveNetflix’s mission to provide seamless entertainment to hundreds of millions of users globally demands exceptional reliability. At the heart of this reliability is how we handle incidents — those inevitable moments when something doesn’t...]]></description>
		
		
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		<title>From Facts &#038; Metrics to Media Machine Learning: Evolving the Data Engineering Function at Netflix</title>
		<link>https://noise.getoto.net/2025/08/21/from-facts-metrics-to-media-machine-learning-evolving-the-data-engineering-function-at-netflix/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Thu, 21 Aug 2025 17:39:40 +0000</pubDate>
				<category><![CDATA[data]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[Technology]]></category>
		<guid isPermaLink="false">https://medium.com/p/6dcc91058d8d</guid>

					<description><![CDATA[By Dao Mi, Pablo Delgado, Ryan Berti, Amanuel Kahsay, Obi-Ike Nwoke, Christopher Thrailkill, and Patricio GarzaAt Netflix, data engineering has always been a critical function to enable the business’s ability to understand content, power recommendation...]]></description>
		
		
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		<item>
		<title>ML Observability: Bringing Transparency to Payments and Beyond</title>
		<link>https://noise.getoto.net/2025/08/18/ml-observability-bringing-transparency-to-payments-and-beyond/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Mon, 18 Aug 2025 18:15:00 +0000</pubDate>
				<category><![CDATA[machine learning]]></category>
		<category><![CDATA[ml-explainability]]></category>
		<category><![CDATA[ml-observability]]></category>
		<category><![CDATA[payment-processing]]></category>
		<category><![CDATA[payments]]></category>
		<guid isPermaLink="false">https://medium.com/p/33073e260a38</guid>

					<description><![CDATA[By Tanya Tang, Andrew MehrmannAt Netflix, the importance of ML observability cannot be overstated. ML observability refers to the ability to monitor, understand, and gain insights into the performance and behavior of machine learning models in producti...]]></description>
		
		
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		<item>
		<title>Accelerating Video Quality Control at Netflix with Pixel Error Detection</title>
		<link>https://noise.getoto.net/2025/08/12/accelerating-video-quality-control-at-netflix-with-pixel-error-detection/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Mon, 11 Aug 2025 21:29:57 +0000</pubDate>
				<category><![CDATA[image-processing]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[quality-assurance]]></category>
		<category><![CDATA[video production]]></category>
		<guid isPermaLink="false">https://medium.com/p/47ef7af7ca2e</guid>

					<description><![CDATA[By Leo Isikdogan, Jesse Korosi, Zile Liao, Nagendra Kamath, Ananya PoddarAt Netflix, we support the filmmaking process that merges creativity with technology. This includes reducing manual workloads wherever possible. Automating tedious tasks that take...]]></description>
		
		
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		<item>
		<title>Behind the Streams: Live at Netflix. Part 1</title>
		<link>https://noise.getoto.net/2025/07/15/behind-the-streams-live-at-netflix-part-1/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Tue, 15 Jul 2025 16:04:08 +0000</pubDate>
				<category><![CDATA[Architecture]]></category>
		<category><![CDATA[live streaming]]></category>
		<category><![CDATA[Netflix]]></category>
		<guid isPermaLink="false">https://medium.com/p/d23f917c2f40</guid>

					<description><![CDATA[Behind the Streams: Three Years Of Live at Netflix. Part 1.By Sergey Fedorov, Chris Pham, Flavio Ribeiro, Chris Newton, and Wei WeiMany great ideas at Netflix begin with a question, and three years ago, we asked one of our boldest yet: if we were to en...]]></description>
		
		
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		<title>Netflix Tudum Architecture: from CQRS with Kafka to CQRS with RAW Hollow</title>
		<link>https://noise.getoto.net/2025/07/10/netflix-tudum-architecture-from-cqrs-with-kafka-to-cqrs-with-raw-hollow/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Thu, 10 Jul 2025 19:31:06 +0000</pubDate>
				<category><![CDATA[cqrs]]></category>
		<category><![CDATA[data architecture]]></category>
		<category><![CDATA[hollow]]></category>
		<category><![CDATA[kafka]]></category>
		<category><![CDATA[tudum]]></category>
		<guid isPermaLink="false">https://medium.com/p/86d141b72e52</guid>

					<description><![CDATA[By Eugene Yemelyanau, Jake GriceIntroductionTudum.com is Netflix’s official fan destination, enabling fans to dive deeper into their favorite Netflix shows and movies. Tudum offers exclusive first-looks, behind-the-scenes content, talent interviews, li...]]></description>
		
		
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		<title>Driving Content Delivery Efficiency Through Classifying Cache Misses</title>
		<link>https://noise.getoto.net/2025/07/02/driving-content-delivery-efficiency-through-classifying-cache-misses/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Wed, 02 Jul 2025 15:20:23 +0000</pubDate>
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					<description><![CDATA[By Vipul Marlecha, Lara Deek, Thiara OrtizThe mission of Open Connect, our dedicated content delivery network (CDN), is to deliver the best quality of experience (QoE) to our members. By localizing our Open Connect Appliances (OCAs), we bring Netflix c...]]></description>
		
		
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		<title>AV1 @ Scale: Film Grain Synthesis, The Awakening</title>
		<link>https://noise.getoto.net/2025/07/02/av1-scale-film-grain-synthesis-the-awakening/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Wed, 02 Jul 2025 14:21:44 +0000</pubDate>
				<category><![CDATA[microservices]]></category>
		<category><![CDATA[streaming]]></category>
		<category><![CDATA[video-encoding]]></category>
		<category><![CDATA[videos]]></category>
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					<description><![CDATA[Unleashing Film Grain Synthesis on Netflix and Enhancing Visuals for MillionsLi-Heng Chen, Andrey Norkin, Liwei Guo, Zhi Li, Agata Opalach and Anush MoorthyPicture this: you’re watching a classic film, and the subtle dance of film grain adds a layer of...]]></description>
		
		
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		<title>Model Once, Represent Everywhere: UDA (Unified Data Architecture) at Netflix</title>
		<link>https://noise.getoto.net/2025/06/12/model-once-represent-everywhere-uda-unified-data-architecture-at-netflix/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Thu, 12 Jun 2025 14:56:32 +0000</pubDate>
				<category><![CDATA[Data Catalog]]></category>
		<category><![CDATA[data-engineering]]></category>
		<category><![CDATA[data-management]]></category>
		<category><![CDATA[Knowledge management]]></category>
		<category><![CDATA[rdf]]></category>
		<guid isPermaLink="false">https://medium.com/p/6a6aee261d8d</guid>

					<description><![CDATA[By Alex Hutter, Alexandre Bertails, Claire Wang, Haoyuan He, Kishore Banala, Peter Royal, Shervin AfsharAs Netflix’s offerings grow — across films, series, games, live events, and ads — so does the complexity of the systems that support it. Core busine...]]></description>
		
		
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