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Choose the right ML task (e.g., classification vs. ranking). Data Preparation: Design the data pipeline, including collection and feature engineering Model Development: Select algorithms and training strategies. Evaluation: Define offline and online metrics like accuracy or latency. Design for deployment, scaling, and real-time inference. Monitoring: Implement mechanisms for tracking model decay and handling data bias Key Case Studies machine learning system design interview pdf alex xu
: Deep dive into object recognition and high-dimensional image data. : Two-tower models, Approximate Nearest Neighbors (ANN), and