The session will explore the often-overlooked backbone of machine learning: efficiency. While most ML practitioners are familiar with using models, fewer understand the underlying principles that make those models scalable, practical, and performant. We'll unpack how optimization, compute constraints, and model design interact to create efficient learning systems and why that matters for both research and real-world deployment.