ML-Powered Search and Recommendation 101

Learn the fundamentals of building machine learning-powered search and recommendation systems in this 23-minute conference talk from Conf42 ML 2025. Discover why search and recommendation systems are crucial for modern applications and explore how to properly evaluate system goals to align with business objectives. Examine comprehensive system architecture patterns and dive deep into the two-stage approach of candidate generation and ranking that forms the backbone of scalable recommendation systems. Understand the complexities of ranking models, including the challenges of feature engineering, model selection, and performance optimization. Master essential design principles for building scalable systems that can handle large-scale data and user interactions. Stay current with the latest trends shaping the future of recommendation systems, from advanced deep learning techniques to emerging architectural patterns. Gain practical insights into implementing these systems effectively while considering real-world constraints and performance requirements.