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Boosting Media & Entertainment Production Efficiency with AI and Cloud

RandomTrees

The media and entertainment sector is being transformed on a new scale owing to technological progression. This article will explore why the integration of AI and cloud computing technologies into the media and entertainment sphere makes the production process more efficient at all stages, from development to marketing.

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The Impact of Generative AI on Media and Advertising

RandomTrees

Generative AI, the most recent advancement of artificial intelligence is changing media and advertising for the better. This article explores the impact of generative AI on the media and advertising industries in terms of content production, audience selection, targeting, and campaigns. The Impact of AI on Media 1.

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Scaling Media Machine Learning at Netflix

Netflix Tech

Our goal in building a media-focused ML infrastructure is to reduce the time from ideation to productization for our media ML practitioners. We accomplish this by paving the path to: Accessing and processing media data (e.g. We accomplish this by paving the path to: Accessing and processing media data (e.g.

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5 Ways Advertising, Media and Entertainment Companies are Using Gen AI

Snowflake

The emergence of generative AI (gen AI) heralds a new, groundbreaking era for advertising, media and entertainment. According to a recent Snowflake report, Advertising, Media and Entertainment Data + AI Predictions 2024 , gen AI is going to transform the industry — from content creation to customer experience.

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Building a Media Understanding Platform for ML Innovations

Netflix Tech

By Guru Tahasildar , Amir Ziai , Jonathan Solórzano-Hamilton , Kelli Griggs , Vi Iyengar Introduction Netflix leverages machine learning to create the best media for our members. Some ML algorithms are computationally intensive. It also provided insights into query patterns and algorithms that were gaining traction among users.

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New Series: Creating Media with Machine Learning

Netflix Tech

By Vi Iyengar , Keila Fong , Hossein Taghavi , Andy Yao , Kelli Griggs , Boris Chen , Cristina Segalin , Apurva Kansara , Grace Tang , Billur Engin , Amir Ziai , James Ray , Jonathan Solorzano-Hamilton Welcome to the first post in our multi-part series on how Netflix is developing and using machine learning (ML) to help creators make better media?—?from

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Implementing the Netflix Media Database

Netflix Tech

In the previous blog posts in this series, we introduced the N etflix M edia D ata B ase ( NMDB ) and its salient “Media Document” data model. NMDB is built to be a highly scalable, multi-tenant, media metadata system that can serve a high volume of write/read throughput as well as support near real-time queries.

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