: Choose appropriate architectures and define loss functions.
An ML system is only as good as its data. Break down your data pipeline into distinct stages:
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[ Billions of Videos ] │ ▼ ┌───────────────────────────┐ │ Candidate Generation │ --> Fast, reduces pool to ~500 videos └───────────────────────────┘ │ ▼ ┌───────────────────────────┐ │ Ranking │ --> Heavy DL Model, scores & sorts top 500 └───────────────────────────┘ │ ▼ [ Final Top 10 Recommended Videos ] 3. Feature Engineering & Storage
[ Raw logs ] → [ ETL (Spark/Beam) ] → [ Feature pipeline ] → [ Training dataset ] [ Model code ] → [ Trainer (TF/PyTorch) ] → [ Model artifact ] → [ Model Registry ] : Choose appropriate architectures and define loss functions
Focus on sparse feature engineering, extreme class imbalance, and low serving latency.
Video tags, upload time, view count, historical click-through rate. The interviewer is not just looking for a
How does the business goal translate to an ML problem? (e.g., binary classification, ranking, regression).
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Translate the business requirements into a concrete machine learning task.
What kind of data is accessible, and do we have labeled data? 2. Framing the ML Problem