Explore lessons and future prospects for machine learning parameterization of sub-grid atmospheric physics from the perspective of emulating cloud superparameterization in this 42-minute conference talk. Delve into the challenges of global modeling, multiskill modeling, and GPU computing in climate science. Examine creative approaches to short simulations, course graining, and feature engineering. Analyze the tradeoffs, generalization strategies, and physical credibility of neural network models in atmospheric physics. Gain insights into hyperparameter tuning, missing information, and the importance of reporting failures in ML-based climate modeling. Conclude with a discussion on cognitive dissonance, excitement, and the future of machine learning in atmospheric science.