I started with data collection and feature engineering, which felt natural, but about 15 minutes in I realized I hadn't touched on serving latency or how predictions would actually get surfaced to users in real time.
Start by clarifying the business objective and success metrics, then outline the end-to-end ML pipeline from data collection to deployment. Focus on feature engineering, model choices, and evaluation specific to watch time prediction, while discussing trade-offs and scalability.
Pro tip: Emphasize that watch time is a proxy for engagement and that you would design the system to optimize long-term user satisfaction, not just immediate clicks. Mention the importance of handling implicit feedback and potential biases in the data.
Define what 'watch time' means (e.g., total minutes watched per session, per user, per title) and the prediction granularity. Identify business goals and success metrics like RMSE, MAE, or ranking metrics if used for recommendations.
Identify data sources: user interactions (views, pauses, skips), content metadata, user demographics, and contextual data. Engineer features such as historical watch time, user activity patterns, content popularity, and temporal features.
Choose appropriate models: start with baselines (e.g., global average, matrix factorization), then consider advanced models like gradient boosted trees, deep neural networks, or sequence models (RNNs, Transformers) for temporal patterns. Address cold-start and scalability.
Use offline evaluation with time-based splits to mimic production. Consider online A/B testing to measure impact on user engagement. Monitor for bias and ensure model robustness.
Design a serving architecture for low-latency predictions, possibly with batch pre-computation. Set up monitoring for data drift, model performance, and business metrics, with retraining pipelines.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.