Start by clarifying the business objective and scale, then outline an end-to-end ML pipeline from data ingestion to serving. Emphasize trade-offs between model choices, latency, and cost, and discuss how to measure success with offline and online metrics.
Pro tip: Show awareness of Netflix's unique content and global scale by discussing multilingual and multimodal aspects, and propose a human-in-the-loop feedback mechanism to continuously improve the model.
Ask questions to understand the volume of posts, latency needs, topic taxonomy, and how classifications will be used (e.g., recommendations, content moderation).
Describe sourcing labeled data (e.g., via crowdsourcing or weak supervision), handling multilingual text, and preprocessing steps like tokenization and normalization.
Compare approaches: traditional (TF-IDF + SVM) vs. deep learning (fine-tuned transformers like BERT). Discuss handling class imbalance and multi-label classification.
Define offline metrics (F1, precision/recall) and online metrics (engagement, CTR). Plan for A/B testing and monitoring for drift.
Outline serving architecture (batch vs. real-time), model optimization (quantization, distillation), and infrastructure for scalability and low latency.
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