Netflix Inc.

Andrew David Eichacker

Prasanna Vijayanathan

Alexander Christian Pavlakis

Farnaz Karimdady Sharifabad

Jason Nicholas Munning

Aryan Mehra

Anselm Lau

Chaïna Wade

A computer-implemented method includes identifying static information associated with a computing device that is running a media playback application. The method also includes monitoring the media playback application during a playback session to identify dynamic information associated with the playback session. Still further, the method includes instantiating a trained machine learning (ML) model to determine, based on historical usage data associated with the media playback application, a likelihood that the media playback application will experience an out of memory kill. The trained ML model implements a graded window to indicate a scaled likelihood that an out of memory kill will occur within a specified timeframe. Then, according to the trained ML model's determination, the method generates a prediction that an out of memory kill will occur for the media playback application within the specified timeframe. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for predicting and mitigating out of memory kills

The disclosed computer-implemented methods and systems leverage machine learning techniques to generate disruption predictions associated with digital media sessions. For example, the methods and systems discussed herein generate input features for a deep neural network that represent various characteristics associated with a session. By applying the deep neural network to the generated input features, the methods and systems described herein generate an accurate disruption prediction in addition to an attribution report detailing which of the characteristics represented among the input features had the greatest impact on the disruption prediction. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for predicting disruptions in digital content system sessions

The disclosed computer-implemented methods and systems leverage machine learning techniques to generate disruption and delight predictions associated with digital media sessions. For example, the methods and systems discussed herein generate input features for a deep neural network that represent various characteristics associated with a session. By applying the deep neural network to the generated input features, the methods and systems described herein generate accurate disruption and delight predictions in addition to an attribution report detailing which of the characteristics represented among the input features had the greatest impact on the generated predictions. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for predicting user experiences during digital content system sessions

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Overland Park, KS, United States of America

Andrew David Eichacker