Review foundational industry papers, including Deep Neural Networks for YouTube Recommendations (Covington et al.) and Ad Click Prediction: a View from the Trenches (McMahan et al.).
A: You need to understand MLOps principles (deployment, monitoring) to design a complete system, but you don't necessarily need to be an MLOps engineer.
Define the goal. Is it a ranking problem or a classification problem? What are the scale requirements (QPS)? Are we optimizing for precision or recall? 2. Data Engineering & Schema In ML, data is king. You must discuss: Where is the raw data coming from? Features: What signals are most predictive?
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Serving architecture (batch vs. online), scaling, and monitoring. 2. Scalability and Latency
Always start by asking:
Systems like Ad Click Prediction, Netflix Recommendations, or DoorDash ETA Estimation. Is it a ranking problem or a classification problem
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Define scale, data volume, and latency requirements. Is this an online system requiring sub-100ms response times, or an offline batch system?
Collaborative filtering vs. Two-tower models. and latency requirements.
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Map business needs to ML objectives:
