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	<title>Netflix Technology Blog &#8211; Noise</title>
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	<link>https://noise.getoto.net</link>
	<description>The collective thoughts of the interwebz</description>
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		<title>Ontology-Driven Observability: Building an End-to-End Knowledge Graph at Netflix Scale</title>
		<link>https://noise.getoto.net/2026/10/16/ontology-driven-observability-building-an-end-to-end-knowledge-graph-at-netflix-scale/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Fri, 16 Oct 2026 16:01:02 +0000</pubDate>
				<category><![CDATA[GenAI]]></category>
		<category><![CDATA[knowledge-graph]]></category>
		<category><![CDATA[observability]]></category>
		<category><![CDATA[ontology]]></category>
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					<description><![CDATA[Prasanna Vijayanathan, Renzo Sanchez-SilvaWhen Jake Paul and Mike Tyson stepped into the ring in November 2024, we delivered the fight to roughly 65 million concurrent streams. On an ordinary day our infrastructure takes in more than 2 billion requests...]]></description>
		
		
		
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		<item>
		<title>Integrating Netflix’s Foundation Model into Personalization applications</title>
		<link>https://noise.getoto.net/2026/10/14/integrating-netflixs-foundation-model-into-personalization-applications/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Wed, 14 Oct 2026 20:02:22 +0000</pubDate>
				<category><![CDATA[embedding]]></category>
		<category><![CDATA[foundation-models]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[Netflix]]></category>
		<category><![CDATA[transformer-model]]></category>
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					<description><![CDATA[Authors: Divya Gadde, Ko-Jen Hsiao, Dhaval Patel and Moumita BhattacharyaThe motivation for building the Netflix Foundation Model for personalization was laid out in this previous blog post. The Netflix homepage is powered by several specialized models...]]></description>
		
		
		
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		<item>
		<title>DataJunction as Netflix’s answer to the missing piece of the modern data stack</title>
		<link>https://noise.getoto.net/2026/10/14/datajunction-as-netflixs-answer-to-the-missing-piece-of-the-modern-data-stack/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Wed, 14 Oct 2026 19:59:08 +0000</pubDate>
				<category><![CDATA[Analytics]]></category>
		<category><![CDATA[data-engineering]]></category>
		<category><![CDATA[modern-data-stack]]></category>
		<category><![CDATA[open source]]></category>
		<category><![CDATA[semantic-layer]]></category>
		<guid isPermaLink="false">https://medium.com/p/92af926b40a5</guid>

					<description><![CDATA[by Olek Gorajek, Samuel Redai, Yian ShangIntroOur Netflix journey from data to dashboards, particularly within the space of the experimentation platform, always presented a unique set of challenges. This blog post aims to illuminate these hurdles and e...]]></description>
		
		
		
			</item>
		<item>
		<title>Measuring the Impact of Personalized Recommendations</title>
		<link>https://noise.getoto.net/2026/10/14/measuring-the-impact-of-personalized-recommendations/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Wed, 14 Oct 2026 19:57:43 +0000</pubDate>
				<category><![CDATA[consumer-behavior]]></category>
		<category><![CDATA[economics]]></category>
		<category><![CDATA[personalization]]></category>
		<category><![CDATA[recommendation-system]]></category>
		<category><![CDATA[recommender-systems]]></category>
		<guid isPermaLink="false">https://medium.com/p/4c26be3a4d96</guid>

					<description><![CDATA[By Kevin Zielnicki, Guy Aridor, Aurélien Bibaut, Allen Tran, Winston Chou, and Nathan KallusIntroductionPersonalization is everywhere on the internet. It shapes the products we discover, the stories we read, the music we hear, and the movies and TV sho...]]></description>
		
		
		
			</item>
		<item>
		<title>TimeSeries Tiered Storage Journey: Kafka/Flink Streams to Native Cassandra Cold Reads</title>
		<link>https://noise.getoto.net/2026/10/14/timeseries-tiered-storage-journey-kafka-flink-streams-to-native-cassandra-cold-reads/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Wed, 14 Oct 2026 19:56:08 +0000</pubDate>
				<guid isPermaLink="false">https://medium.com/p/e59d597c9d60</guid>

					<description><![CDATA[By Oleksii Tkachuk, Rajiv Shringi, Chris Lohfink, Abe Ratnofsky, Kartik SathyanarayananIntroductionIn an earlier post, we introduced the TimeSeries Abstraction, our high-throughput, low-latency platform designed to store and query temporal event data a...]]></description>
		
		
		
			</item>
		<item>
		<title>Behind the Scenes: Evolving Netflix’s Ads Event Pipeline for Live — Part II</title>
		<link>https://noise.getoto.net/2026/10/14/behind-the-scenes-evolving-netflixs-ads-event-pipeline-for-live-part-ii/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Wed, 14 Oct 2026 19:51:35 +0000</pubDate>
				<guid isPermaLink="false">https://medium.com/p/826ebf9ad9fb</guid>

					<description><![CDATA[By Yogesh NagarurIntroductionIn Part 1 of this series, we shared how Netflix built the Ads Event pipeline and approached it as a system design problem: replacing fragmented pipelines with a centralized collection, enrichment, and a standard data contra...]]></description>
		
		
		
			</item>
		<item>
		<title>Netflix Conductor: The Next Chapter</title>
		<link>https://noise.getoto.net/2026/10/14/netflix-conductor-the-next-chapter/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Wed, 14 Oct 2026 19:50:17 +0000</pubDate>
				<category><![CDATA[cassandra]]></category>
		<category><![CDATA[distributed-systems]]></category>
		<category><![CDATA[netflix-conductor]]></category>
		<category><![CDATA[Orchestration]]></category>
		<category><![CDATA[workflow]]></category>
		<guid isPermaLink="false">https://medium.com/p/41ad21067649</guid>

					<description><![CDATA[Netflix Conductor : The Next Chapterby Aravindan Ramkumar on behalf of the Conductor teamConductor is the workflow orchestration engine Netflix uses to stitch micro-services into reliable, observable business processes.If you’ve followed Conductor from...]]></description>
		
		
		
			</item>
		<item>
		<title>Running Apache Spark experiments in my sleep (and on a plane)</title>
		<link>https://noise.getoto.net/2026/10/14/running-apache-spark-experiments-in-my-sleep-and-on-a-plane/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Wed, 14 Oct 2026 19:49:11 +0000</pubDate>
				<category><![CDATA[Apache Spark]]></category>
		<category><![CDATA[Data Pipeline]]></category>
		<category><![CDATA[generative ai tools]]></category>
		<category><![CDATA[performance-optimization]]></category>
		<guid isPermaLink="false">https://medium.com/p/588816b6b5f8</guid>

					<description><![CDATA[By Prashanth Gedde NarayanaswamyAs one of Netflix’s largest and critical data pipelines, member-sessionizer consolidates tens of billions of hourly client events, encompassing user taps, playbacks, and engagement signals, into individual viewing sessio...]]></description>
		
		
		
			</item>
		<item>
		<title>The Lifecycle of LLM-as-a-Judge: Building, Aligning, and Monitoring at scale</title>
		<link>https://noise.getoto.net/2026/10/14/the-lifecycle-of-llm-as-a-judge-building-aligning-and-monitoring-at-scale/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Wed, 14 Oct 2026 19:48:49 +0000</pubDate>
				<category><![CDATA[AI]]></category>
		<category><![CDATA[human-in-the-loop]]></category>
		<category><![CDATA[LLM]]></category>
		<category><![CDATA[llm-as-a-judge]]></category>
		<category><![CDATA[recommendation-system]]></category>
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					<description><![CDATA[By Emma Yanyang Kong, JJ Tan, Ishan Gupta, Lars Olds, Claire Campbell, David Fagnan, Ratna Kavuri, Rohan Gosain, Veli Balin, Minsu Jang, Louis GarciaIntroductionAny team generating text with an LLM at scale hits the same problem. You cannot evaluate ev...]]></description>
		
		
		
			</item>
		<item>
		<title>Netflix Stratum Media Processing: Automated Container Right-Sizing</title>
		<link>https://noise.getoto.net/2026/10/14/netflix-stratum-media-processing-automated-container-right-sizing/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Wed, 14 Oct 2026 19:48:20 +0000</pubDate>
				<category><![CDATA[cloud computing]]></category>
		<category><![CDATA[cost optimization]]></category>
		<category><![CDATA[resource-management]]></category>
		<category><![CDATA[serverless]]></category>
		<category><![CDATA[software engineering]]></category>
		<guid isPermaLink="false">https://medium.com/p/ddf9aa68a801</guid>

					<description><![CDATA[By Violetta Pidvolotska and Naveen MareddyStratum is Netflix’s internal serverless media processing platform, shaped by over a decade of experience developing large-scale distributed systems. It powers a wide range of media workloads — including encodi...]]></description>
		
		
		
			</item>
		<item>
		<title>Reliable GPU compute for large training &#038; inference jobs</title>
		<link>https://noise.getoto.net/2026/10/14/reliable-gpu-compute-for-large-training-inference-jobs/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Wed, 14 Oct 2026 19:47:42 +0000</pubDate>
				<category><![CDATA[artificial intelligence]]></category>
		<category><![CDATA[cost-efficiency]]></category>
		<category><![CDATA[GPU]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[site reliability]]></category>
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					<description><![CDATA[Authors: Nick Rutigliano, Argha C, Samartha ChandrashekarHow we detect failing GPUs and network fabric, remediate machines automatically, and let long-running jobs survive hardware loss.Open Netflix, and the home screen is assembled for you: which rows...]]></description>
		
		
		
			</item>
		<item>
		<title>Trading a Cloud Identity for Your Own: Workload Attestation on Managed Compute</title>
		<link>https://noise.getoto.net/2026/09/25/trading-a-cloud-identity-for-your-own-workload-attestation-on-managed-compute/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Fri, 25 Sep 2026 16:01:03 +0000</pubDate>
				<category><![CDATA[Apache Spark]]></category>
		<category><![CDATA[aws-emr]]></category>
		<category><![CDATA[cloud]]></category>
		<category><![CDATA[Identity]]></category>
		<category><![CDATA[security]]></category>
		<guid isPermaLink="false">https://medium.com/p/516d5a29b252</guid>

					<description><![CDATA[By Dhruv PratapIntroductionOrganizations that have been around for a while usually run two identity systems side by side. One belongs to the cloud provider: IAM roles, instance profiles, execution roles. The other is your own, and it is the one your in...]]></description>
		
		
		
			</item>
		<item>
		<title>Leave the Class Path in the Rearview Mirror</title>
		<link>https://noise.getoto.net/2026/09/18/leave-the-class-path-in-the-rearview-mirror/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Fri, 18 Sep 2026 16:01:02 +0000</pubDate>
				<category><![CDATA[java]]></category>
		<category><![CDATA[JVM]]></category>
		<category><![CDATA[software engineering]]></category>
		<category><![CDATA[software-development]]></category>
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					<description><![CDATA[Introducing composable, module system native and agent friendly command line tools for modern Java developmentBy Danny Thomas, JVM Ecosystem TeamRecent work on the Java language to pave the on-ramp has made it easier than ever to start a Java program a...]]></description>
		
		
		
			</item>
		<item>
		<title>MAPS: Netflix’s Multimodal Asset Personalization at Scale</title>
		<link>https://noise.getoto.net/2026/08/28/maps-netflixs-multimodal-asset-personalization-at-scale/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Fri, 28 Aug 2026 16:01:02 +0000</pubDate>
				<category><![CDATA[embedding]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[multimodal]]></category>
		<category><![CDATA[Netflix]]></category>
		<category><![CDATA[personalization]]></category>
		<guid isPermaLink="false">https://medium.com/p/32f96320785e</guid>

					<description><![CDATA[By Emma Yanyang Kong, Aditya Deshpande, Asad Abbasi, Bowei Yan, David Fagnan, Ashish Rastogi, Dhaval Patel, Ray ZhangIntroductionThe Netflix experience is a journey of discovery. Every visual cue, from the artwork on a title to the video previews that ...]]></description>
		
		
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			</item>
		<item>
		<title>A Tale of Two Flink Autoscalers</title>
		<link>https://noise.getoto.net/2026/08/21/a-tale-of-two-flink-autoscalers/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Fri, 21 Aug 2026 16:01:01 +0000</pubDate>
				<category><![CDATA[apache flink]]></category>
		<category><![CDATA[autoscaling]]></category>
		<category><![CDATA[operational excellence]]></category>
		<category><![CDATA[stream-processing]]></category>
		<guid isPermaLink="false">https://medium.com/p/e9f6a1b1492b</guid>

					<description><![CDATA[Samuel Yeboah, Francesco Di Chiara and Mingliang LiuToday, Netflix runs two Flink autoscalers. That is exactly one more than we want. We built the first one in-house years ago, when there was no mature option suited to our platform. The second came fro...]]></description>
		
		
		<enclosure url="" length="0" type="" />

			</item>
		<item>
		<title>How and Why Netflix Built a Real-Time Distributed Graph: Part 3 — Querying the graph with gRPC…</title>
		<link>https://noise.getoto.net/2026/08/07/how-and-why-netflix-built-a-real-time-distributed-graph-part-3-querying-the-graph-with-grpc/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Fri, 07 Aug 2026 16:01:02 +0000</pubDate>
				<guid isPermaLink="false">https://medium.com/p/0f3468349607</guid>

					<description><![CDATA[How and Why Netflix Built a Real-Time Distributed Graph: Part 3 — Querying the graph with gRPC execution APIAuthors: Nilesh Mishra and Ajit KotiThis is the third entry of a multi-part blog series describing how we built a Real-Time Distributed Graph (R...]]></description>
		
		
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			</item>
		<item>
		<title>Modeling Device Capabilities for Analytics</title>
		<link>https://noise.getoto.net/2026/07/31/modeling-device-capabilities-for-analytics/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Fri, 31 Jul 2026 16:01:02 +0000</pubDate>
				<category><![CDATA[Analytics]]></category>
		<category><![CDATA[data-engineering]]></category>
		<category><![CDATA[data-modeling]]></category>
		<category><![CDATA[devices]]></category>
		<guid isPermaLink="false">https://medium.com/p/e7607acebde8</guid>

					<description><![CDATA[by Aarti Laddha, Richard Diaz-Cool, Rishika Idnani, Venkatesh SelverajNetflix supports a vast and evolving set of features and content types, ranging from 4K streaming and immersive audio to live streaming and cloud gaming, across a diverse ecosystem o...]]></description>
		
		
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			</item>
		<item>
		<title>GenRec: Towards LLM-Native Recommendation at Netflix</title>
		<link>https://noise.getoto.net/2026/07/30/genrec-towards-llm-native-recommendation-at-netflix/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Thu, 30 Jul 2026 20:10:15 +0000</pubDate>
				<category><![CDATA[GenAI]]></category>
		<category><![CDATA[large language models]]></category>
		<category><![CDATA[LLM]]></category>
		<category><![CDATA[Netflix]]></category>
		<category><![CDATA[recommendation-system]]></category>
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					<description><![CDATA[Authors: Ying Li, Arjun Rao, Shradha SehgalIntroductionRecommendations sit at the heart of the Netflix experience. Our current production models rely on thousands of hand‑crafted features over users, items, and interactions, along with specialized arch...]]></description>
		
		
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			</item>
		<item>
		<title>In-House LLM Serving at Netflix</title>
		<link>https://noise.getoto.net/2026/07/18/in-house-llm-serving-at-netflix/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Fri, 17 Jul 2026 21:32:39 +0000</pubDate>
				<category><![CDATA[AI]]></category>
		<category><![CDATA[LLM]]></category>
		<category><![CDATA[model-serving]]></category>
		<category><![CDATA[Netflix]]></category>
		<category><![CDATA[Platform Engineering]]></category>
		<guid isPermaLink="false">https://medium.com/p/a5a8e799ea2c</guid>

					<description><![CDATA[By AI Platform’s Model Runtime team and Inference teamIntroductionMost organizations consume LLMs through hosted APIs. Netflix went further — we run the full stack ourselves, from model deployment through inference, inside our existing production envir...]]></description>
		
		
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		<item>
		<title>Building Service Topology at Scale: Architecture, Challenges, and Lessons Learned</title>
		<link>https://noise.getoto.net/2026/07/14/building-service-topology-at-scale-architecture-challenges-and-lessons-learned/</link>
		
		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Mon, 13 Jul 2026 22:44:11 +0000</pubDate>
				<category><![CDATA[backend-development]]></category>
		<category><![CDATA[data-engineering]]></category>
		<category><![CDATA[distributed-systems]]></category>
		<category><![CDATA[observability]]></category>
		<category><![CDATA[software engineering]]></category>
		<guid isPermaLink="false">https://medium.com/p/f4b792f3f0d8</guid>

					<description><![CDATA[By Parth Jain, Rakesh Sukumar, Yingwu Zhao, Renzo Sanchez-Silva &#38; Nathan FisherA deep dive into the engineering challenges of building a real-time service dependency map at Netflix scale: from streaming architectures and distributed aggregation pip...]]></description>
		
		
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