In this talk, we review the topology design process used by data scientists and explain why 1st order methods are computational expensive in the design process. We explore the benefits of 2nd order methods to reduce the topology design cost and highlight recent work that approximates the inverse Hessian. We conclude with recommendations to accelerate the adoption of these methods in the DL ecosystem.