It is becoming increasingly evident that many machine learning problems may be reduced to submodular optimization. Previous work addresses generic discrete approaches and specific relaxations. In this work, we take a generic view from a relaxation perspective. We show a relaxation formulation and simple rounding strategy that, based on the monotone closure of relaxed constraints, reveals analogies between minimization and maximization problems, and includes known results as special cases and extends to a wider range of settings. Our resulting approximation factors match the corresponding integrality gaps. For submodular maximization, a number of relaxation approaches have been proposed. A critical challenge for the practical applicability of these techniques, however, is the complexity of evaluating the multilinear extension. We show that this extension can be efficiently evaluated for a number of useful submodular functions, thus making these otherwise impractical algorithms viable for real-world machine learning problems.