Revolutionizing Media with Machine Learning: A Glimpse into Netflix’s Technological Evolution
In a detailed blog post by the Netflix Technology team, the intricacies of scaling media machine learning at Netflix are unveiled. The article, penned by a group of experts including Gustavo Carmo, Elliot Chow, and Nagendra Kamath, among others, delves deep into the journey of Netflix from offering streaming alongside DVD shipping services in 2007 to leveraging machine learning models for personalizing artwork and creating promotional content efficiently.
The primary goal of building a media-focused machine learning infrastructure is to streamline the process from ideation to productization for media ML practitioners. This involves facilitating easy access and processing of media data, efficient training of large-scale models, and productizing these models in a self-serve manner to work on existing and newly arriving assets. The infrastructure also aims to store and serve model outputs for promotional content creation.
The article highlights the unique challenges media ML practitioners face and the solutions devised to address them. One such solution is the development of Jasper, a unified library that standardizes media assets and enables seamless access to various types of media data. Another significant component is the Amber Feature Store, which reduces costs and promotes reuse by memoizing features/embeddings tied to media entities.
The post also discusses the Amber Compute, a suite offering triggering capabilities to initiate the computation of algorithms with recursive dependency resolution, and the development of a large-scale GPU training cluster based on Ray, which enhances the training system throughput by 3-5 times.
Furthermore, the article presents a case study on scaling match cutting, a video editing technique, using the media ML infrastructure. It outlines the initial challenges, such as lack of standardization and wasteful repeated computations, and how the media machine learning infrastructure provided the necessary building blocks to overcome these hurdles.
In conclusion, the post explores the promising prospects at the intersection of media and machine learning, with plans to extend the infrastructure to facilitate a growing set of use cases, including ML-based VFX tooling and improving recommendations using a suite of content understanding models.
To delve deeper into this technological evolution, read the full article on Netflix Technology Blog.