Evaluation in Machine Learning does not always get the attention it deserves. I hope to focus our attention for the time of this talk on the questions of systematic evaluation in machine learning and the changes that we should continue to make as we elevate the standard of evaluation across our field. The breadth of application areas we collaborate on in machine learning requires a variety of approaches for evaluation, and we’ll explore this variety by considering applications in generative models, social good, healthcare, and environmental science. Grounded in these applications, we will expand the conceptual aperture through which we think about machine learning evaluations, starting from purely technical evaluations (thinking about likelihoods), moving to mixed methods (with proper scoring rules and expert assessments), and then to sociotechnical assessments (considering fairness, impacts, and participation). My core message is that broad and expansive evaluation remains fundamental and an area into which I hope we will drive even greater investments as a community, together.