This paper discusses the implementation and performance improvements of deep neural networks in YouTube's recommendation system. The system is divided into two main components: a deep candidate generation model and a deep ranking model. The candidate generation model uses deep learning to retrieve a small set of relevant videos from a vast corpus based on user activity history, while the ranking model assigns scores to these videos to determine the most engaging ones for presentation. The paper highlights the challenges of scale, freshness, and noise in recommendation systems and how deep learning addresses these issues. It also provides practical insights and lessons learned from designing and maintaining a large-scale recommendation system. The authors detail the architecture and training methods of both models, emphasizing the importance of feature engineering, depth, and heterogeneous signals. The paper concludes with a discussion on the effectiveness of deep learning in improving recommendation performance and the trade-offs involved in model complexity.This paper discusses the implementation and performance improvements of deep neural networks in YouTube's recommendation system. The system is divided into two main components: a deep candidate generation model and a deep ranking model. The candidate generation model uses deep learning to retrieve a small set of relevant videos from a vast corpus based on user activity history, while the ranking model assigns scores to these videos to determine the most engaging ones for presentation. The paper highlights the challenges of scale, freshness, and noise in recommendation systems and how deep learning addresses these issues. It also provides practical insights and lessons learned from designing and maintaining a large-scale recommendation system. The authors detail the architecture and training methods of both models, emphasizing the importance of feature engineering, depth, and heterogeneous signals. The paper concludes with a discussion on the effectiveness of deep learning in improving recommendation performance and the trade-offs involved in model complexity.