What features will the model use? Categorize them into user features, item features, and contextual features (time of day, device).
Never start drawing architecture boxes immediately. Begin by asking clarifying questions to establish both business and technical constraints. machine learning system design interview alex xu pdf github
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Ingesting raw logs, orchestrating ETL (Extract, Transform, Load) processes, storing features in a Feature Store, and executing distributed training. Begin by asking clarifying questions to establish both
Age, country, historical watch history (last 5 videos, last 30 days).
: Ask clarifying questions to understand the business goal (e.g., maximize clicks vs. revenue), scale (DAU, data volume), and latency constraints. Problem Framing
With the rise of mobile ML (CoreML, TensorFlow Lite), you might discuss keeping the model on the phone to preserve user privacy and reduce server load. The trade-off? Updating the model requires a user to download a new app version.