Deep Dives

In many ML design scenarios, challenges around model supervision are common. These include issues such as noisy labels, limited labeled data, or labels that are only indirectly related to the target tasks. Demonstrating the ability to apply self-supervised and semi-supervised learning techniques to tackle these problems can significantly differentiate candidates by showcasing data-efficient and robust model designs. Among these techniques, contrastive learning stands…

User representation modeling is a critical component of personalized recommendation systems, making it both representative and scalable is a key challenge frequently discussed in ML system design interviews. At WWW '24, Meta published an insightful paper[1] on their online user modeling framework, which presents valuable design discussions and considerations. In this post, I’ll deep-dive this work using the Machine Learning System Design structure…