Real-time Retrieval with Deep Learning: Benefits and Challenges

Deep Learning is disrupting decades' worth of data infrastructure building. Specifically, we will look at retrieval problems. These are core to similarity search, deduplication, recommender systems, matching, feed ranking, personalization, and many more. While deep learning can unlock greater relevance and accuracy, it also presents significant operational and scientific challenges. Collecting data (correctly) is far from being trivial because of complex data interactions, training new models requires scale and distribution that most companies are not comfortable using, and real-time serving (efficiently) is almost impossible with tools that exist today. We will deep-dive into how HyperCube is thinking about unifying these applications under a single abstraction and a unified system.