: Planning for online inference, scalability, and infrastructure (e.g., cloud vs. on-premise).
Before writing a single line of pseudo-code or choosing a model, the candidate must define the problem. This involves asking clarifying questions: Is this batch or real-time? What is the latency requirement (100ms vs. 10 seconds)? What is the prediction ceiling (e.g., what is the maximum possible accuracy given noisy data)? Successful candidates translate vague business goals into concrete ML tasks—classification, regression, ranking, or clustering. Aminian’s PDF often includes checklists for this phase, ensuring the candidate does not prematurely jump to model selection. This involves asking clarifying questions: Is this batch
While different versions exist, the canonical steps are: What is the prediction ceiling (e
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? While different versions exist