Explore data-driven and data-assisted modeling techniques for fluid dynamics and geophysics applications in this conference talk from the Machine Learning for Climate KITP conference. Dive into the challenges of predicting chaotic dynamical systems and high-resolution forecasting. Examine the benefits of machine learning in fluid modeling and discover hybrid architectures that combine traditional numerical methods with data-driven approaches. Learn about computational costs, initial prototypes, and real-world applications in numerical weather prediction. Gain insights into multi-time step optimization and data assimilation techniques. Engage with technical questions and discussions on the potential of these innovative modeling approaches for advancing climate science and Earth system understanding.