Start by clarifying the business objective and constraints, then walk through the ML system design in a structured manner covering problem formulation, data, features, modeling, training, inference, and rollout. Emphasize how the system balances host autonomy with algorithmic recommendations and how you would measure success through experiments.
Pro tip: Focus on the human-in-the-loop aspect: hosts can override recommendations, so design the system to provide explainable suggestions and measure host acceptance rate as a key metric. Also, discuss how you would handle cold-start for new listings by leveraging similar listings and geographic data.
Ask questions to understand the goal: increase bookings, host revenue, or occupancy? Define the ML problem as a regression or ranking task, and specify the prediction target (e.g., optimal price per night) and constraints (e.g., host-set minimums).
Identify data sources: listing attributes, host behavior, booking history, search logs, competitor prices, seasonality, local events. Engineer features like location embeddings, amenity scores, demand indices, and host responsiveness.
Choose models (e.g., gradient boosted trees for tabular data, deep learning for text/images) and design a training pipeline with periodic retraining, validation, and hyperparameter tuning. Consider multi-task learning to predict both price and demand.
Design a low-latency inference service that can handle real-time requests from hosts, with caching for frequent queries. Use a feature store to ensure consistency between training and serving.
Plan a phased rollout: start with a small percentage of hosts, run A/B tests to measure impact on bookings and revenue, and iterate based on feedback. Include guardrail metrics to avoid negative host experiences.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.