Remove Architecture Remove Entertainment Remove Metrics
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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. By collecting and analyzing key performance metrics of the service over time, we can assess the impact of the new changes and determine if they meet the availability, latency, and performance requirements.

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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.

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Telltale: Netflix Application Monitoring Simplified

The Netflix TechBlog

A metric crossed a threshold. Metrics are a key part of understanding application health. But sometimes you can have too many metrics, too many graphs, and too many dashboards. Telltale uses a variety of signals from multiple sources to assemble a constantly evolving model of the application’s health: Atlas time series metrics.

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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. Overall Architecture The Data Mesh system can be divided into the control plane (Data Mesh Controller) and the data plane (Data Mesh Pipeline).

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Unbundling the Graph in GraphRAG

O'Reilly

The haphazard results may be entertaining, although not quite based in fact. While RAG leverages nearest neighbor metrics based on the relative similarity of texts, graphs allow for better recall of less intuitive connections. RAG provides a way to “ground” answers within a selected set of content. Does GraphRAG improve results?

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Experimentation is a major focus of Data Science across Netflix

The Netflix TechBlog

Growth Advertising At Netflix, we want to entertain the world ! A Type-M error occurs when, given that we observe a statistically-significant result, the size of the estimated metric movement is magnified (or exaggerated) relative to the truth. Are there metrics that can yield a signal faster? What’s the tradeoff of using those?

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Beyond REST

The Netflix TechBlog

Rapid Development with GraphQL Microservices by Dane Avilla The entertainment industry has struggled with COVID-19 restrictions impacting productions around the globe. Consumers of GraphQL APIs can simply leverage the open-source GraphQL client of their preference.

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