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Title Launch Observability at Netflix Scale

The Netflix TechBlog

Part 3: System Strategies and Architecture By: VarunKhaitan With special thanks to my stunning colleagues: Mallika Rao , Esmir Mesic , HugoMarques This blog post is a continuation of Part 2 , where we cleared the ambiguity around title launch observability at Netflix. The request schema for the observability endpoint.

Traffic 180
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Data Mesh?—?A Data Movement and Processing Platform @ Netflix

The Netflix TechBlog

A Data Movement and Processing Platform @ Netflix By Bo Lei , Guilherme Pires , James Shao , Kasturi Chatterjee , Sujay Jain , Vlad Sydorenko Background Realtime processing technologies (A.K.A stream processing) is one of the key factors that enable Netflix to maintain its leading position in the competition of entertaining our users.

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Foundation Model for Personalized Recommendation

The Netflix TechBlog

This scenario underscored the need for a new recommender system architecture where member preference learning is centralized, enhancing accessibility and utility across different models. To harness this data effectively, we employ a process of interaction tokenization, ensuring meaningful events are identified and redundancies are minimized.

Tuning 165
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Helping VFX studios pave a path to the cloud

The Netflix TechBlog

Rachel Kelley (AWS), Ranjit Raju (AWS) Rendering is core to the the VFX process VFX studios around the world create amazing imagery for Netflix productions. This ultimately results in more compelling entertainment for Netflix members. By: Peter Cioni (Netflix), Alex Schworer (Netflix), Mac Moore (Conductor Tech.),

Cloud 291
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Netflix Studio Engineering Overview

The Netflix TechBlog

In an effort to effectively and efficiently produce this content we are looking to improve and automate many areas of the production process. We combine our entertainment knowledge and our technical expertise to provide innovative technical solutions from the initial pitch of an idea to the moment our members hit play.

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Unbundling the Graph in GraphRAG

O'Reilly

The haphazard results may be entertaining, although not quite based in fact. While the overall process may be more complicated in practice, this is the gist. For example, a mention of “NLP” might refer to natural language processing in one context or neural linguistic programming in another. Does GraphRAG improve results?

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Supporting content decision makers with machine learning

The Netflix TechBlog

To mitigate this uncertainty, executives throughout the entertainment industry have always consulted historical data to help characterize the potential audience of a title using comparable titles, if they exist. Similar titles In entertainment, it is common to contextualize a new project in terms of existing titles.