This document discusses making Netflix machine learning algorithms reliable. It describes how Netflix uses machine learning for tasks like personalized ranking and recommendation. The goals are to maximize member satisfaction and retention. The models and algorithms used include regression, matrix factorization, neural networks, and bandits. The key aspects of making the models reliable discussed are: automated retraining of models, testing training pipelines, checking models and inputs online for anomalies, responding gracefully to failures, and training models to be resilient to different conditions and failures.