Use of AI in Streaming Platforms

To gain an edge over their competitors, streaming platforms are focusing their efforts on providing high-quality streaming services using AI and machine learning technology.

Fremont, CA: The pandemic has had a huge mark on the entertainment sector. As a result, these platforms' audiences have grown by orders of magnitude in the previous few years. More individuals will pay for online streaming services than for a television set-top box in the coming years. However, the streaming market is more competitive than it has ever been. As a result, to personalize the viewing experience, every streaming provider uses Artificial Intelligence.

The pandemic has had a huge mark on the entertainment sector. As a result, these platforms' audiences have grown by orders of magnitude in the previous few years. More individuals will pay for online streaming services than for a television set-top box in the coming years. However, the streaming market is more competitive than it has ever been. As a result, to personalize the viewing experience, every streaming provider uses Artificial Intelligence.

Uses of AI in streaming services:

Personalized thumbnail

A streaming service must persuade a viewer that a title is worthwhile to watch. One method is to use a related thumbnail to represent the titles. However, finding an image that is both relatable and shows the qualities of a title is tough. Netflix employs contextual bandits to overcome this problem. The term "contextual bandits" refers to a type of online learning system. It chooses the best artworks to appeal to a wide range of genre-specific consumers. For example, the platform takes into account a variety of audience characteristics such as titles clicked, genres of titles, viewer interactions with a single title, country, language preferences, and more.

Live streaming

Imagine watching a cricket match live and seeing a player strike a high-flying shot but only being able to see a pixelated catch out; the bad network would enrage many sports fans. In fact, adaptive bitrate (ABR) streaming is to blame for this. If a user is watching a video at 1080 pixels and their internet speed suddenly dips, ABR automatically adjusts to 480 pixels. Beyond ABR, several OTT platform owners are working to improve streaming. Deep learning is one approach to accomplish this. The system is fed past mobile network statistics as well as data from the video environment. By adapting to reduced pixels, the system could give a more immersive experience for the user.

Content quality optimization

The experience of a viewer can be ruined by an unoptimized video. As a result, it's critical to guarantee that video, audio, and text are of high quality. The Quality Control process at Netflix is divided into two categories: automated inspection and manual inspection. Before and after the encoding procedure, automated inspections are carried out. By injecting data from previous manual QC tests, automated inspections adopt a supervised machine learning (ML) approach. The data is then used to train a predictive quality control model that predicts whether a test will pass or fail. The model's primary purpose is to identify any problematic assets, even if this necessitates additional manual tests.