Remove Efficiency Remove Entertainment Remove Tuning
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Foundation Model for Personalized Recommendation

The Netflix TechBlog

It facilitates the distribution of these learnings to other models, either through shared model weights for fine tuning or directly through embeddings. In NLP, the trend is moving away from numerous small, specialized models towards a single, large language model that can perform a variety of tasks either directly or with minimal fine-tuning.

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Title Launch Observability at Netflix Scale

The Netflix TechBlog

The Insight TriadAPI To efficiently understand the health of a title and triage issues quickly, all implementations of the observability endpoint must answer: is the title eligible for this phase of promotion, if notwhy is it not eligible, and what can be done to fix any problems. The request schema for the observability endpoint.

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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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A Recap of the Data Engineering Open Forum at Netflix

The Netflix TechBlog

At Netflix, we aspire to entertain the world, and our data engineering teams play a crucial role in this mission by enabling data-driven decision-making at scale. To handle errors efficiently, Netflix developed a rule-based classifier for error classification called “Pensive.” Until next time!

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

The Netflix TechBlog

stream processing) is one of the key factors that enable Netflix to maintain its leading position in the competition of entertaining our users. More Processing Patterns And Better Efficiency People use Data Mesh not only to move data. Please stay tuned! They often also want to process or transform their data along the way.

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Unbundling the Graph in GraphRAG

O'Reilly

The haphazard results may be entertaining, although not quite based in fact. Also, in place of expensive retraining or fine-tuning for an LLM, this approach allows for quick data updates at low cost. This latter approach with node embeddings can be more robust and potentially more efficient. at Facebook—both from 2020.

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Migrating Critical Traffic At Scale with No Downtime?—?Part 2

The Netflix TechBlog

Behind these perfect moments of entertainment is a complex mechanism, with numerous gears and cogs working in harmony. They enable us to further fine-tune and configure the system, ensuring the new changes are integrated smoothly and seamlessly. But what happens when this machinery needs a transformation?

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