Since the seminal work of Gatheral, Jaisson, and Rosenbaum (2014), it has become widely accepted that volatility in derivatives markets exhibits rough behavior.  Numerical methods for rough volatility models are often difficult to stabilize and scale to large volatility smile surfaces.  In addition, calibration to volatility surfaces, rather than to historical data, remains a significant challenge, as does the reliable valuation of exotic derivatives.  In this work, we clarify how machine learning tools can be used most effectively to address these problems.

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