: Identify relevant features and strategies for handling missing values or imbalanced data.

: Offers a step-by-step approach to navigate complex ML design problems, starting from problem definition to final deployment. Real-World Case Studies

: Returning similar images using contrastive learning embeddings. Recommendation Engines

In the competitive landscape of AI engineering, by Ali Aminian and Alex Xu has emerged as a cornerstone resource. This guide moves beyond simple algorithms to address the architectural complexity of deploying ML at scale. The 7-Step Framework for ML Design

: Predicting click-through rates (CTR) at massive scale.

If you are preparing for a specific interview soon, I can help you (like a News Feed or Fraud Detection system) or summarize a chapter for you. Which system design problem are you most interested in?