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Scaling Media Machine Learning at Netflix

The Netflix TechBlog

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.

Media 290
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MezzFS?—?Mounting object storage in Netflix’s media processing platform

The Netflix TechBlog

Mounting object storage in Netflix’s media processing platform By Barak Alon (on behalf of Netflix’s Media Cloud Engineering team) MezzFS (short for “Mezzanine File System”) is a tool we’ve developed at Netflix that mounts cloud objects as local files via FUSE. MezzFS has a number of features, including: Stream objects ?— ?

Media 214
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Designing Instagram

High Scalability

User Feed Service, Media Counter Service) read the actions from the streaming data store and performs their specific tasks. media search index, locations search index, and so forth) in future. After that, the post gets added to the feed of all the followers in the columnar data storage. After that, the various services (e.g.

Design 334
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How To Implement Video Information and Editing APIs in Java

DZone

Not only has near-infinitely scalable cloud storage reduced the burden of storing large video files, but CDNs (content delivery networks) deployed by video streaming and social media giants in this timeframe have all but eliminated those slow server-to-client buffering times, which initially plagued the user experience.

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Netflix Cloud Packaging in the Terabyte Era

The Netflix TechBlog

By Xiaomei Liu , Rosanna Lee , Cyril Concolato Introduction Behind the scenes of the beloved Netflix streaming service and content, there are many technology innovations in media processing. Packaging has always been an important step in media processing. Uploading and downloading data always come with a penalty, namely latency.

Cloud 237
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Building In-Video Search

The Netflix TechBlog

Video embeddings are precomputed on the shot level, stored in our media feature store , and replicated to an elastic search cluster for real-time nearest neighbor queries. The embedding computation is based on a large neural network model and has to be run on GPUs for optimal throughput.

Media 225
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Edge Data Platforms, Real-Time Services, and Modern Data Trends

DZone

You may also know that this has led to an increase in the demand for efficient and secure data storage solutions that won’t break the bank. Edge data platforms are software solutions that enable businesses to collect, process, and analyze data at the edge of the network.

IoT 130