Scaling Media Machine Learning at Netflix
In 2007, Netflix started offering streaming alongside its DVD shipping services. As the catalog grew and users adopted streaming, so did the opportunities for creating and improving our recommendations. With a catalog spanning thousands of shows and a diverse member base spanning millions of accounts, recommending the right show to our members is crucial. Why should members care about any particular show that we recommend? Trailers and artworks provide a glimpse of what to expect in that show. We have been leveraging machine learning (ML) models to personalize artwork and to help our creatives create promotional content efficiently.
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:In this post, we will describe some of the challenges of applying machine learning to media assets, and the infrastructure components that we have built to address them. We will then present a case study of using these components in order to optimize, scale, and solidify an existing pipeline. Finally, we’ll conclude with a brief discussion of the opportunities on the horizon.In this section, we highlight some of the unique challenges faced by media ML practitioners, along with the infrastructure components that we have devised to address them.
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