The concept of deepaesthetics is proposed as both naming the experience of machine learning configured by statistics, computer science, and platform culture, and as a way to register a different sensibility for AI. The data science concepts of “depth” and “layers” and statistical functions for “dimensionality reduction” are critically explored. Examining their sociotechnical dimensions, bequeathed via their statistical genealogy and contemporary computational infrastructure and power, machine learning experience is analyzed as tending toward a control assembly or agencement. Artful probing of machine learning’s sensibility is proposed, instead, to enable different encounters with computational experience. Process philosophy, both historical and contemporary, is introduced as a means for conceptually probing an alternative sensibility for machine learning, redolent with strange indeterminacies.