Marketplace AI/ML Applications and Product Vision Questions

Discussion of how machine learning capabilities are developed and applied across a consumer marketplace or two-sided product portfolio, including practical deployment considerations, ML architectures, experimentation, product strategy, and governance for ML-enabled features such as search ranking, dynamic pricing, recommendations, image recognition and quality classification, and fraud detection. Covers the end-to-end production ML lifecycle (data collection, feature engineering, training, A/B experimentation, canary/shadow deployment, monitoring, retraining), feature stores and training-serving consistency, offline vs online evaluation, and how these systems are designed to align with product strategy at scale.