Netflix uses machine intelligence to provide the greatest content for our subscribers. We previously detailed one of these algorithms in depth, how our platform team is expanding the media-specific machine learning ecosystem, and how data from these algorithms is saved in our annotation service.

The majority of the ML literature is concerned with model training, assessment, and scoring. In this post, we will look at an understudied element of the ML lifecycle: model output integration into applications.

We will specifically look at the infrastructure that provides Netflix's search capabilities for studio apps. We talk about particular difficulties we solved with Machine Learning (ML) algorithms, distinct pain points we addressed, and a technical overview of our new platform.

Overview

We at Netflix strive to make our users happy by providing them with access to exceptional content. This event consists of two parts. First, we must supply them with stuff that will make them happy. Second, we must make it simple and straightforward to select items from our library. In the member experience, we must swiftly surface the most notable highlights from the titles accessible on our site in the form of photographs and videos.

Here's an example of a similar asset made for one of our titles:

These multimedia components, often known as "supplemental" assets, do not appear out of nowhere. They must be created by artists and video editors. We create creator tools so that these colleagues may devote their time and attention to creation. Unfortunately, most of their energy is spent on laborious pre-work. One significant possibility is to automate these routine chores.

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